Human beings have long told versions of the same warning: Be careful what you wish for.

In Greek mythology, King Midas got exactly what he asked for, but at the cost of everything else he valued. In the famous story of The Monkey’s Paw, a man’s wishes are granted through terrible and unforeseen routes.

These stories feel newly relevant with the rise of artificial intelligence agents, systems to which we can give a goal, then leave them to work out how to get there.

As AI systems become more autonomous, they are coming to resemble wish-granting genies that find routes and use methods we did not imagine from incomplete instructions.

This problem, known as AI alignment, was foreseen in theory as early as 1960. It has hovered in the background of AI research ever since—but as recent events have shown, the alignment problem is now both real and urgent.

Achieving the Goal but Missing the Point

During a recent OpenAI cybersecurity evaluation, frontier AI agents were asked to solve some benchmark test problems. They broke out of the testing environment, reached the internet, inferred that another company might hold the solutions, and attacked its systems.

This is an extreme example of “specification gaming”: achieving the measurable objective while defeating the purpose of the task.

The incident shows how intermediate, or “instrumental,” goals can become dangerous. The AI systems did not “want power” but gained access, resources, and freedom as a means to reach the final goal (solving the test problems).

Finding Loopholes

The same problem has appeared in mundane settings. In Australia, a user asked a personal AI assistant to book gym classes.

The agent found the gym’s booking software did not actually enforce the restrictions it showed to human viewers. So the agent booked further ahead than it should have been able to, and when asked to move its user up a waitlist, it cancelled somebody else’s reservation.

The user had not told it to do this. Persistent AI can quickly find loopholes and pursue routes its human users never intended.

Adding more rules might seem like an easy solution: don’t hack third parties, don’t cancel other people’s bookings, don’t do anything harmful. These may help, but we cannot predict every route a capable agent might discover. And even a clear rule depends on understanding when it applies.

The Context Problem

In a third recent incident, Anthropic reported cyber evaluations in which agents were told they were inside a simulation. But they were mistakenly given access to real systems.

One model noticed evidence it might be on the open internet but reasoned the systems could still be part of the exercise and continued attacking. The context had changed, but the agent stuck with its original task.

Context can fail in reverse too. During the OpenAI incident, Hugging Face—the company attacked by OpenAI’s agents—tried to use frontier AI models to analyze what had happened.

But the safety guardrails on the AI models blocked the requests, because they couldn’t tell the users were trying to defend against attacks rather than commit them. The safeguards were well-intentioned, but without enough context, they produced behavior misaligned with the user’s legitimate intent.

So alignment depends on context and authority. How much judgment should be built into an AI model by its maker? And how much should come from a separate supervisory system? And finally, who should control that supervision: the maker, or the organization or country responsible for the outcome?

AI Guarding AI

One response to the first question comes from AI pioneer Yoshua Bengio. His Scientist AI proposal aims to build a powerful supervisory AI system to watch over agents. Instead of pursuing goals itself, it would estimate what is true and what consequences a proposed action might have, acting as a guardrail around more agentic systems.

In wish-story terms, before letting the genie out of the bottle, the supervisory AI would ask it to explain how it plans to grant the wish. Then it would ask a human or another AI to inspect the plan carefully.

Anticipating every surprising strategy is hard. But once a plan says “cancel somebody else’s booking,” recognizing the problem is much easier.

Who Watches the Watcher?

But can we trust the supervisory AI? It can still be wrong.

Alignment cannot depend on one AI becoming perfectly trustworthy. My colleagues and I at CSIRO, Australia’s national science agency, are working with the Australian AI Safety Institute on one aspect of this broader challenge.

At CSIRO, we envisage combining AI supervisors with software rules, cyber-security controls, human strengths, monitoring, reversible actions, and human approval for critical steps. The aim is to correlate different sources of evidence rather than trust any single approach.

This is a “sociotechnical systems” approach to AI safety and alignment, rather than just a technical one.

Control is another question. Organizations and countries may need to govern these supervisory systems themselves instead of leaving them to an overseas AI provider.

The old wish stories gave people one chance to get the wish right. With AI, we can do better. We can check the goal, inspect the means, constrain what the system can do, watch what it does, and retain sovereign control over the power to intervene and stop it.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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People can accept tradeoffs when they see value — but if they don’t, what happens?

The post Understanding Anti-AI Public Opinion appeared first on Towards Data Science.

This article was originally published on Undark. Read the original article.

In June, a community of mostly mathematicians released the Leiden Declaration on Artificial Intelligence and Mathematics, an articulation of the values they hope to preserve as automated systems are integrated into the practice of developing mathematical proofs. This is necessary because some frontier AI systems have shown striking capabilities for solving certain advanced mathematics problems, though independent tests show that AI still has important limits.

Although I’m not a member of the pure mathematics community in any strict sense, much of the declaration’s message resonated with me and was relevant to my own research interests as a computational biologist.

I’m encouraged that the mathematics community decided to take a stand on the issue and that it has been successful in organizing a large number of eminent mathematicians to sign the document. The Leiden Declaration, which originated at a conference held at Leiden University in the Netherlands, should spawn proper copycats, because what is true for mathematics is true for virtually every field that calls itself as a science. The inventions of mathematics percolate into the algorithms and statistical methods that help scientists design experiments, build simulations, and analyze data, from sociology to statistical physics and beyond.

I argue that biological fields should consider something of the sort, because the kinds of knowledge that biology generates and predicts are uniquely vulnerable to subversion and mischaracterization by artificial intelligence.

The conversation in the mathematics community has been illuminating, in that the declaration is a coordinated response to the powers and risks of AI, whose acceleration has felt like a Thanos snap, changing the universe in an instant. And part of the reason that mathematicians felt the effects so immediately is tied to the manner in which their research is conducted: A mathematical proof, in principle, is transparent and independently verifiable, and no proprietary equipment is (generally) required to check it.

The Leiden Declaration should spawn proper copycats, because what is true for mathematics is true for virtually every field that calls itself as a science.

As the Leiden Declaration notes, automated techniques now present mathematics with a new forgery problem: Because mathematical truths are fixed and verifiable, one can identify a counterfeit formalism by comparing it to the genuine proof. Biology, however, offers no such guarantee; our so-called “truths” are often noisy and context-dependent, making it nearly impossible to define what an authentic version should even look like. Some of the most widely appreciated biological principles (such as Mendel’s laws of genetic inheritance) are better described as powerful but limited in scope, and with well-characterized exceptions that don’t undermine the laws but refine their application. This is true for many biological theories. Boundary conditions, edge cases, and noise are not bugs but features of how the natural world works.

For example, a mutation that confers drug resistance to a virus with one genetic background may have a much weaker, neutral, or even harmful effect in another, because its impact depends strongly on the surrounding genetic context. This phenomenon, which biologists call epistasis, is not an exotic edge case but a powerful force across the biosphere in shaping the relationship between an organism’s genes and its expressed characteristics. And epistasis is just one of many examples of context dependence in biological systems, in which a finding that holds true in a dish falls apart in a body or has an effect in a mouse model but not in a primate.

When it comes to AI, the mathematician fears producing a counterfeit solution. But the biologist often cannot say, even acting in the fullest good faith, what the authentic version is supposed to look like.

Despite the differences between mathematics and biology, the life sciences should consider embarking on an exercise that is at least analogous to the Leiden Declaration. If nothing else, a biology version could borrow its structure and ambition. We should insist that researchers disclose their use of automated tools, that they are responsible for the veracity of their findings, that credit and accountability belong to people rather than to systems, and that early-career scientists be protected from incentives that prioritize high-volume output over genuine scientific insight.

A biology declaration should adopt these ideas and others, and emphasize additional provisions that are important to the field: the validation of AI-generated hypotheses against results from the wet lab, the management of training data drawn from the biological commons, and the heightened scrutiny owed to any model whose outputs will eventually touch a patient or an ecosystem. The last point is crucial: In the biomedical realm, AI’s missteps and triumphs will manifest in living bodies, with all of the associated corporeal, emotional, ethical, and legal consequences.

We should insist that researchers disclose their use of automated tools, that they are responsible for the veracity of their findings, that credit and accountability belong to people rather than to systems.

A biological Leiden Declaration must appreciate the nature of the data and observation in biology, and other particulars of the field. But the most important feature for responsibly managing the relationship between AI and living systems involves the durability of what is produced.

A mathematical declaration can aspire for permanence because verified proofs can remain valid across centuries. Biological understandings, on the other hand, tend to shift over time, sometimes rapidly. A policy hastily calibrated to the models of this summer might already be miscalibrated by winter. A declaration written in the hope of lasting a decade could risk spending much of that decade catching up.

If we are to orchestrate a responsible treatise for artificial intelligence in the life sciences, it should be adaptive: versioned, dated, revisited on a published schedule, and amended in the open by the very community it claims to represent. And because different subfields of biology have unique challenges—cardiology versus forest ecology, for instance—perhaps we need multiple declarations (but not too many).

The Leiden Declaration incorporates some of these elements, clarifying that its content reflects AI technologies and mathematical practice as of May 2026 and that updates on the document will be shared. Biology should make that feature part of the central architecture, wiring the process of revision into the document, so that updating it becomes a positive action rather than a confession of failure.

Fortunately, life scientists are well equipped for this task. We have long understood that structures unable to change with their environments rarely endure. It would therefore be a strange betrayal of our discipline to write a declaration that forgets this basic principle. Our policies for technological change must keep the dynamism of living systems at the center of how we imagine the future of biology.

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Policy debates continue over how best to regulate artificial intelligence (AI) and harness its economic and other benefits while safeguarding society from its harms and risks.  For example, the European Union and China have opted to govern AI through different regulatory approaches, while the United States and some other countries have adopted different policy tools that they believe will better promote AI innovation. While countries and regions take different approaches, consensus is emerging across jurisdictions and among leading experts that there is an AI evaluation gap that must be promptly closed. 

Today’s AI evaluations fall short

The 2026 International AI Safety Report, prepared by more than one hundred experts and supported by more than 30 countries and multilateral organisations, explains that today’s AI evaluation techniques often fail to anticipate real-world performance.  This can occur when AI models produce overinflated test results or when AI testing environments materially differ from the real world.  Compounding the challenge, the “AI evidence dilemma” arises from the difficulties of assessing the risks of this rapidly evolving technology.

Boosting AI trust, diffusion, security and investment returns

Closing the AI evaluation gap will bring many benefits.  Sound AI evaluations would help increase understanding of AI’s performance and reliability and better inform decisions about its use.  This is critical since AI’s performance remains “jagged,” with some AI applications performing better than others.  

Helping people better understand AI’s reliability across contexts would enhance their trust in its appropriate use.  Similarly, reliable evaluations can also help buttress the security of AI systems.  All this, in turn, would help to support greater adoption and diffusion of secure and trusted AI applications and help organisations more fully reap the benefits of their AI investments.  

Reliable evaluation helps to reduce uncertainty for policymakers 

Better evaluation provides a greater evidence-based to inform AI policy choices. As explained in the 2026 International AI Safety Report, this could help reduce some of the uncertainty policymakers currently face.  Equipping policymakers with this information could lead to swifter, more confident policy decisions and help regulatory frameworks keep pace with AI’s rapid technological progress.  

Better evaluation could decrease operational costs and expand competition and access

Closing the AI evaluation gap could have the added benefit of reducing AI operating costs, potentially making trusted AI cheaper and more accessible.  Even with mature AI evaluations, there could be variation across jurisdictions on whether they are applied in a voluntary or mandatory manner.  However, if the evaluations underpinning different regulatory approaches are standardised, with little variation across regions, this could help companies and organisations reduce costs and increase ease of operating across borders.  In other words, reliable and standardised evaluation could help to foster both regulatory interoperability and innovation.  This, in turn, could lower barriers for new AI market entrants and support a healthy competition ecosystem.

Governments want better AI evaluations

Already, several governments are investing in closing the AI evaluation gap.  The White House AI Action Plan calls for building an evaluation ecosystem. In June 2026, US President Trump signed a new AI Executive Order establishing a voluntary framework for the government to test covered frontier models to improve secure innovation and cybersecurity.

These actions build on other important government-led AI evaluation efforts.  Following the establishment in 2023 of AI Safety Institutes by the UK and the US[SR1] [SR2]  (both renamed in 2025), a total of 11 jurisdictions, including the EU, India and Singapore, have now followed a similar model and established AI safety institutes or similar organisations that conduct testing and evaluation of foundation models.  Avenues have been paved for international co-ordination among these organisations.

Prior to the new US AI Executive Order, the US Center for AI Standards and Innovation (CAISI), mentioned above, announced voluntary agreements with xAI, Google, and Microsoft for pre-deployment frontier model testing.  These add to CAISI’s voluntary testing arrangements with Anthropic and OpenAI, as well as its collaborations with these companies to boost AI security and related measurement techniques.  The US National Institute of Standards and Technology (NIST) has evaluation programmes for generative AI.  Similarly, Korea and Singapore recently concluded joint tests of AI agents to evaluate data leakage.    The UK AI Security Institute also continues its cutting-edge frontier AI model evaluations.

The private sector is also expanding evaluation initiatives

After discovering that Mythos could detect severe vulnerabilities in all major web browsers and operating systems, Anthropic launched Project Glasswing to make the unreleased frontier model available to several organisations to help secure their systems.  Anthropic went a step further and committed to sharing its learnings with the broader community.  Additionally, several major AI developers launched the Frontier Model Forum (FMF), a collective effort to advance AI safety and security, and have released several publications, including a recent report, Managing Advanced Cyber Risks in Frontier AI Models.

A 5-step roadmap for closing the AI evaluation gap

To successfully close the AI evaluation gap, AI actors can take several steps, building on today’s existing efforts.  

 Step 1: Balance standardisation and customisation

First, to account for different languages, cultures, use cases, and norms, the evaluations should strive to balance standardisation and customisation.  At the 2026 AI Impact Summit in India, several companies pledged to improve multilingual and contextual evaluations to help achieve this balance.  

Step 2: Test throughout the AI lifecycle

Second, to address AI performance differences between controlled environments and the real world, evaluations should be conducted throughout the AI system lifecycle.  This approach is already embraced in several key publications and leading frameworks, including the International AI Safety Report and NIST’s AI Risk Management Framework.  The next step is to develop and implement evaluations for these different contexts.  

Step 3:  Build the right ecosystem 

Third, evaluations must be supported by a robust ecosystem, including qualified examiners.  This should be accompanied by methodologies for effectively communicating AI evaluation results to diverse audiences, including business users and affected individuals. At the same time, evaluations must preserve proprietary information about the AI systems.  Existing assurance practices used in the financial services and other sectors could further inform this work.  

Step 4:  Consider the AI value chain, technology and context.

Fourth, AI evaluations should be tailored to the needs of different actors in the AI value chain and different types of AI deployments.  For example, testing conducted upstream by large language model (LLM) developers may vary from the evaluations performed by companies deploying LLMs downstream.  In other words, the role an organisation plays in the AI ecosystem, including whether it enhances models downstream, should help determine the types of evaluations it implements.  

The rise of AI agents and Agentic AI capable of acting autonomously presents new evaluation challenges that governments and other stakeholders are working to address.  This reinforces the need to continuously assess the suitability of evaluation methodologies for different AI technologies and deployment settings.  

Step 5:  Create an efficient and trusted process

Finally, the process for developing AI evaluations also merits careful consideration.  To help ensure that evaluations address the appropriate factors, the process should capture global inputs from diverse stakeholders, including industry, government, academia and civil society.  To help keep pace with AI’s rapid development, the process should also leverage and co-ordinate the good work already being done. This includes the efforts of safety institutes and standards organisations, such as the “Zero Draft” project launched by NIST to expedite the standards process.  It should also consider the outputs of the OECD Hiroshima AI Process (HAIP) Reporting Framework.  A key purpose of the evaluation process is to  instill trust in everyone affected by a given AI system. 

The time is now

In sum, while much work remains to close the AI evaluation gap, as discussed above, there is already an emerging consensus, a solid foundation, and momentum to do so.  Closing the evaluation gap holds great promise of increasing AI trust, adoption, security, diffusion and investment returns.  Furthermore, it can help reduce policy uncertainty, increase regulatory interoperability, reduce costs for AI companies and organisations, and expand the availability of AI services and competition.  Simply put, the prize is worth the effort. 

The post A five-step roadmap to closing the AI evaluation gap appeared first on OECD.AI.

Scientists just transformed a living mouse’s kidney into an incubator for developing human sperm made from blood cells.

It sounds like sci-fi Mad Libs. But a team at the University of Pennsylvania, led by Kotaro Sasaki, pulled it off. For up to nine months, a tiny pouch of human cells nestled beneath a mouse’s kidney gradually developed into immature sperm. The study is the latest in a decade-long quest to grow sperm in the lab.

If successful, lab-grown sperm could open a new window into the earliest stages of sperm development, a process that’s notoriously difficult to study because it begins before birth. The research could also shed light on male infertility—which, in many cases, has no clear cause—and inspire treatments.

More controversially, lab-grown sperm could one day be used to make babies, offering hope to people struggling to conceive and same-sex couples who want to have children genetically related to both parents. That goal is still far off. Though gene activity was similar to their natural counterparts, none of the lab-grown cells were able to develop into functional sperm.

Those results starkly contrast similar attempts in mice. Researchers have already produced functional sperm and egg cells from rodent skin cells, and in two pioneering cases, used them to create healthy pups with two dads. But translating this capability to humans has been difficult, largely because reproductive development differs tons between species.

Still, the new system can help scientists probe the earliest stages of human sperm development. And because any future clinical applications would first need extensive testing in non-human primates, the team also generated immature sperm cells from monkeys, whose reproductive biology more closely mirrors our own.

Recapitulating sperm development in the lab has uses beyond fertility treatment too, such as testing whether drugs interfere with reproduction. The platform “establishes a robust framework for modeling primate germ cell [reproductive cell] development,” the team wrote.

Winning Recipe

For decades, scientist have been able to rewind adult cells into induced pluripotent stem cells (iPSCs). These cells can go on to  become nearly any other cell type. But steering them to become sperm has proven far trickier, largely because human sperm takes years to fully develop.

The journey begins before birth. Early stem cells give rise to spermatogonia, the founder cells that replenish sperm throughout life. These cells are largely dormant until puberty, when some begin meiosis, a special type of cell division that halves their chromosomes. That way, when sperm meets egg, the embryo gains a full genetic set.

But the cells don’t live in a vacuum. Proteins and other molecules instruct immature sperm when to grow, divide, or pause. Physical forces, such as the winding architecture of the testes and the flow of fluid, also play a role. Recreating this intricate environment in a dish has been one of the biggest challenges to the study of sperm development and our ability to grow them in the lab.

Roughly a decade ago, Sasaki and colleagues found a way to transform human iPSCs into early stem cells that could eventually give rise to sperm and egg. On paper, their gene expression profile closely matched that of natural counterparts. But in practice, the cells couldn’t mature further without the right environmental cues.

In an usual workaround, the team next mixed the immature cells with supportive, non-reproductive cells isolated from mice testes. While it was an usual environment, the mice cells provided nutrients and molecular signaling that nudged development forward.

Called xrTestis, the mixture spontaneously organized into tube-like structures resembling those inside testes. “Overall, our culture method accurately recapitulates in vivo human male GC [germ cell] development and allows us to understand the genetic pathways governing this process,” they wrote at the time.

Yet none of the immature sperm advanced beyond developmental stages normally seen in fetuses. And the miniature structure collapsed after 80 days, likely because it lacked a blood supply.

Unexpected Host

To prolong the mixture’s viability and push sperm development further, the team transplanted it into the kidneys of immunodeficient mice.

The graft organized itself into the hallmark tubular structures found in testes within a month and remained stable for at least half a year. The mice showed no signs of discomfort or immune rejection.

Six months later, some human cells developed into spermatogonia—the self-renewing stem cells that eventually generate sperm. Along the way, they underwent a major event: an epigenetic reset. During this process, chemical tags on DNA that influence whether genes are turned on or off are almost completely wiped clean. If that reset is incomplete, it could compromise any sperm eventually used for reproduction.

Here, the team found a “dramatic” genome-wide epigenetic reset. The cells’ gene activity mirrored their natural counterparts. Even though the graft survived for at least nine months, however, none of the cells were able to develop into mature sperm.

This is likely due to the environment. Human and mice testes don’t share the exact same signaling molecules or respond the same way to hormones and other developmental cues. Replacing the mouse support cells with human versions could help the spermatogonia develop further.

The Ultimate Test

The team also tested the technique in monkeys, with results similar to those found in human cells. “While our human iPSC system provided valuable insight into male gametogenesis [the formation of reproductive cells], future studies of fertility competency must be carried out in non-human primates,” they wrote.

Although the cells also halted at the immature stage, the results are still valuable. Previous studies have shown monkey spermatogonia can generate mature sperm after transplantation into recipient testes, opening the door to eventually testing if lab-grown cells can sire healthy offspring.

That idea is precisely what makes some bioethicists uneasy.

Mass-producing sperm and eggs in the lab could generate far more embryos for selection, making it easier for prospective parents to choose desirable traits such as eye color or height. Pairing the technology with gene editing makes “designer babies” less hypothetical. And if skin scrapings or a single hair can be turned into reproductive cells, someone could theoretically create sperm or eggs from another person without consent.

These scenarios are purely speculation, but regulators are already preparing for that future. In 2025, the United Kingdom’s Human Fertilization and Embryology Authority urged the government to explicitly tackle lab-grown reproductive cells in legislation. The International Society for Stem Cell Research has similarly called for careful oversight and public engagement before clinical use. Most countries, however, are only beginning to grapple with how these technologies should be dealt with.

Meanwhile, companies are pressing forward. Paterna Biosciences in Utah recently announced they had produced functional sperm from immature sperm collected during testicular biopsies. According to the company, early embryos created with the lab-grown sperm seemed comparable to those produced through standard in vitro fertilization (IVF). And California startup Conception recently reported generating early human egg cells from iPSCs. Neither company has released results in a preprint or journal article, making the claims hard to evaluate.

Like germline gene editing, conversations weighing the pros and cons of lab-grown reproductive cells will help decide not only what’s possible, but also what should be permitted. For now, the team stresses that their work is only a research tool—not a fertility treatment—and clinical use is a long way off.

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This three-part blog series explores the growing complementarity between artificial intelligence (AI) and quantum technologies. The first post introduced quantum technologies and outlined their strengths and the challenges of combining AI with quantum systems. The second examined how AI can support the development of quantum technologies, helping to optimise systems and accelerate progress towards practical applications. In this third and final instalment, we turn to the reverse relationship: how quantum technologies – including computing, sensing and communication – could support the future evolution of AI systems.

Although large-scale, fault-tolerant quantum computers remain a long-term goal, early-stage quantum devices and their integration with existing AI systems are already paving the way for quantum-enhanced AI. These developments could eventually lead to meaningful improvements across many sectors of the economy and in daily life.

What technical breakthroughs could quantum computing bring to AI?

In recent years, AI systems have become more powerful and data-intensive, particularly through large language models (LLMs). These developments have made the limitations of classical computing, especially in speed, energy consumption and scalability, increasingly apparent. Quantum technologies could offer a pathway to expand the frontiers of classical computing, potentially overcoming today’s computing bottlenecks. However, quantum computers are not expected to replace existing AI systems. They are likely to coexist, with quantum systems excelling at solving specific types of problems that are difficult or intractable for conventional computers.

As quantum computing evolves, it is expected to strengthen AI in two main ways. First, quantum computers could significantly improve energy efficiency. Training AI requires substantial computing resources that consume vast amounts of electricity, raising economic, energy security, and environmental concerns. This is particularly true of LLMs.

These pressures apply more broadly across statistical AI techniques, such as machine learning, where increasing model complexity and data intensity are pushing the limits of classical computing. This is often characterised by experts as the slowing of or end to Moore’s Law.

Second, quantum computing could enhance the performance and capabilities of AI systems. This has fuelled growing interest in quantum machine learning (QML), which refers to machine learning methods implemented using quantum algorithms. QML is expected to improve AI system learning performance in areas such as optimisation, pattern recognition and high-dimensional data analysis.

In the long term, QML may reduce the computational requirements and energy footprint of some AI workloads by performing complex computations more efficiently, although this remains to be demonstrated at scale. QML remains in an early stage of development and faces three key bottlenecks:

• Data transfer constraints: Moving large volumes of classical data (bits) into and out of quantum systems (qubits) remains slow, limiting the suitability for data-intensive AI tasks.
• Unproven advantage: Demonstrating performance gains over highly optimised algorithms on classical computers remains challenging.
• Unclear hardware requirements: Defining hardware specifications for QML, including qubit counts and coherence times, remains difficult, making it challenging to develop practical QML roadmaps.

For these reasons, experts do not expect widespread commercial QML applications within the next decade.

Near-term pathways to AI applications

While QML is a longer-term prospect, quantum-inspired AI techniques are already delivering practical benefits. These approaches adapt concepts from quantum physics for use on classical computing hardware. One prominent example is the use of tensor networks, originally developed to simulate quantum systems, to compress LLMs.

These techniques have significant practical implications for AI developers. As neural networks such as LLMs become larger and more complex, deployment is constrained not only by fixed hardware limits such as memory and processing capacity but also by the computational and energy costs of training and running these systems.

Tensor-network compression to reduce memory use

Some research has shown that tensor-network compression can reduce memory use and computational demands by 10-100x, often with only modest reductions in accuracy after fine-tuning. These efficiency gains enable advanced AI models to run on conventional CPUs, edge devices and legacy infrastructure, expanding access beyond specialised high-performance computing environments.

Because these techniques do not require quantum hardware, they are already being embedded in commercial applications. Startups and technology providers are offering tensor-network-based compression tools and positioning them as a path to lower costs and energy consumption while improving deployment flexibility.

In practice, quantum-inspired compression is best understood as complementary to established methods such as pruning and quantisation for optimising neural network efficiency. Because they target different forms of redundancy in neural networks, tensor approaches can be combined with conventional techniques and, in some cases, outperform them in both efficiency and performance. Together, these developments illustrate how insights from quantum information science are already shaping the evolution of AI, even before large-scale quantum computers become widely available.

Hybrid quantum-classical computing

The most realistic near-term pathway for combining AI with actual quantum computers is through hybrid quantum-classical systems that leverage the strengths of both AI and quantum computing. In these systems, quantum processors perform specific sub-tasks, such as optimisation or simulation, while classical AI models handle data processing, interpretation and control. In this direction, several computing infrastructures are integrating quantum processors into high-performance computing environments, enabling researchers and industry actors to experiment with quantum-enhanced AI workflows. At the same time, cloud-based quantum platforms are lowering access barriers, allowing AI developers to test quantum algorithms, such as optimisation or sampling routines, that can be integrated into machine learning workflows.

These advances could eventually translate into practical applications across multiple sectors. In materials science and chemistry, quantum computing combined with AI may accelerate the discovery of new materials and drugs by enabling more accurate simulations of molecular behaviour. In manufacturing and logistics, hybrid quantum-AI approaches could improve the performance of complex optimisation tasks such as scheduling, resource allocation and supply chain planning. Financial services actors are also exploring quantum-enhanced modelling for portfolio optimisation and risk analysis, where large combinatorial search spaces pose challenges for classical AI methods.

How could quantum sensing expand AI’s possibilities, and where?

Beyond computing, quantum technologies may also enhance AI through advances in sensing. Quantum sensors can detect extremely small changes in magnetic fields, temperature, motion, or chemical composition, often with higher precision, stability, or spatial resolution than classical devices, thereby producing novel data streams. For AI systems, access to richer and more accurate data can translate directly into new applications across multiple sectors.

In healthcare, more sensitive sensing technologies could enable earlier disease detection and more accurate monitoring of health indicators. By capturing subtle biological or chemical changes that might otherwise go unnoticed, quantum sensors could provide richer data for AI systems to analyse, supporting faster diagnosis and more personalised treatment decisions.

In agriculture, improved sensing precision may help monitor soil conditions, crop health and environmental variables in greater detail, allowing AI tools to optimise irrigation, fertilisation and resource use for higher yields and greater efficiency. Similar approaches could support environmental monitoring, in which higher data quality can strengthen forecasting models and inform policy responses to climate and sustainability challenges.

Quantum sensing also has potential applications in infrastructure and industry. Sensors capable of detecting minute physical changes could help identify early signs of structural stress or equipment degradation in bridges, transport systems or energy facilities. When combined with AI-driven predictive maintenance, this information could enable earlier interventions, reduce operational disruptions and improve safety outcomes. More broadly, the integration of advanced quantum sensing with AI highlights an important dimension of technological progress: improvements in data quality and reliability can be just as transformative as advances in computing capabilities.

How could quantum communication support AI?

Quantum communication could enable secure networking conditions for federated learning and multi-agent systems, where multiple devices collaboratively train models without sharing raw data. Quantum-secure communication channels could make it easier for different parties to share sensitive information (e.g., in healthcare, finance, or critical infrastructure) while reducing the risk of interception or data leakage, especially when training or inference occurs in distributed cloud environments. Research and patent applications are exploring whether entanglement-enabled networks or quantum-secured links could support distributed training architectures across geographically separated computing resources. Although these concepts remain largely experimental, early prototypes are already exploring secure distributed machine learning architectures built on quantum communication protocols.

Quantum communication may also become important for integrating AI with quantum sensing systems. Some advanced quantum sensors generate information directly in quantum states, which cannot always be measured or transmitted using conventional classical channels without losing valuable information. Quantum networking could allow these states to be transferred between devices or processing nodes while preserving their quantum properties, enabling more sophisticated analysis pipelines that combine sensing, computation and AI-driven interpretation.

Leveraging quantum and AI complementarities for a shared technological future

This three-part series examined the potential and challenges of combining AI and quantum technologies, from foundational concepts to emerging applications and future pathways. As we have seen, AI is also accelerating progress in quantum technologies themselves, for example, by improving calibration, noise reduction and experimental design. This bidirectional relationship (AI for quantum and quantum for AI) is likely to shape the next phase of innovation in both fields.

As outlined in this series, the integration of AI and quantum technologies could reshape scientific discovery, healthcare, industry and sustainability. At the same time, significant challenges remain, including technical limitations, talent shortages at the interface between the two technologies, ethical considerations and the need for international collaboration.

As we stand at the early stages of this transformation, one conclusion is clear: the digital future will not be built by AI or quantum technologies alone, but rather through their interplay and collaboration.

Learn more about the OECD work on quantum technologies: www.oecd.org/en/topics/sub-issues/quantum-technologies.html

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Scientists at Columbia University have used a precise gene-editing tool, base editing, to make changes in three disease-linked genes in early-stage human embryos. The goal wasn’t to create pregnancies, but to test the safety and limits of rewriting DNA at the very early stages of life.

The paper, not yet peer reviewed, sparked immediate controversy. Some researchers hailed it as a technical milestone that could one day prevent devastating inherited diseases before birth. Others warned it edges society closer to the prospect of “designer babies”—an idea bioethicists have argued is akin to modern eugenics.

The debate is hardly hypothetical. The work has already attracted commercial interest. New York-based Nucleus Genomics, which screens in vitro fertilization (IVF) embryos for serious genetic disorders, has also developed predictive models for complex traits such as intelligence. The company plans to sponsor future research by study leader Dieter Egli and team.

Critics worry that even experimental advances could fuel demand from wealthy patients while encouraging companies to develop and market embryo-editing technologies, despite unresolved ethical and safety concerns.

Egli argues the findings should be public precisely because these debates are no longer academic curiosity. He has repeatedly called for scientists, regulators, and the public to weigh the pros and cons of editing human embryos. As for clinical use today, his position is unequivocal: “You can’t use it. It’s as clear as day and night,” he told Nature.

Conceptual Shift

Why edit embryos at all?

Cells in an early embryo eventually give rise to every tissue in the body. Correct a harmful mutation at the start of development, and the fix could, in theory, propagate throughout a child’s entire body—and even be passed on to future generations.

The strategy could help in genetic disorders that hamper fetal development or trigger diseases in newborns. For some developmental and metabolic conditions, intervention after birth may already be too late. Even when treatment is possible, gene editors must be able to target various organs, which is an ongoing challenge.

In various efforts, scientists have already repaired disease-causing mutations in mouse embryos and fetuses, including those linked to blood disorders. But mice aren’t humans. Early embryos from the two species repair DNA damage in fundamentally different ways, making it tough to gauge whether a strategy that works in mice will succeed, or prove safe, in people. That uncertainty has fueled interest in testing gene-editing tools directly in human embryos.

Not everyone is on board. International scientific groups have repeatedly called for a temporary ban on editing human embryos, and the practice is illegal in several countries.

That didn’t stop Chinese scientist He Jiankui. In 2018, he announced the birth of gene-edited babies after using a tool called CRISPR-Cas9, claiming the changes would protect them against HIV infection. Global outrage ensued.

By then, years of research had already highlighted CRISPR’s risk. The tool cuts both strands of DNA and relies on the body’s repair machinery to stitch them back together. But the process can go awry, introducing unintended mutations, deleting large chunks of DNA, or altering the wrong locations on the DNA strands altogether. He’s reckless experiment resulted in three years of imprisonment, although he still defends the work.

Subsequent studies only deepened concerns. In some cases, CRISPR editing in human embryos caused extensive genetic damage. In one study,  it completely destroyed the chromosome that housed the target gene.

An Imperfect Upgrade

The new study tested a next-generation gene editor designed to overcome some of CRISPR’s biggest shortcomings.

Egli and team used an approach called base editing, which rewrites individual DNA letters. Unlike CRISPR, base editing only nicks the DNA strands and is generally thought to be more precise. The technology hit a major milestone last year when it helped cure a baby with a potentially fatal genetic disorder, and earlier lab studies hinted it could also succeed in human embryos.

Working with early-stage embryos, the team edited three genes with the potential to cause illness. In each case, they converted the genetic letter A to G at precise locations. One of the genes, PCSK9, regulates “bad” cholesterol levels. Mutations are associated with a high risk of heart problems. The team’s edit was designed to switch off the gene, mirroring strategies already being explored in adults.

The other two targets, HBG1 and HBG2, control production of fetal hemoglobin, an oxygen-carrying protein. The edits made here reflected a natural protective variant that could lessen symptoms in blood disorders, such as sickle cell disease and beta thalassemia.

The team found no signs of widespread DNA damage, suggesting the tool is more precise than CRISPR. But it wasn’t perfect. Many embryos emerged as so-called genetic mosaics, with some cells carrying the intended edit and others retaining their original genetic blueprint.

That’s a huge problem. As an embryo develops, unedited cells could outcompete edited ones, leaving the disease-causing mutation largely intact. In some embryos, edited cells stopped dividing altogether.

And a lack of obvious chromosome damage doesn’t guarantee safety. The edits could still trigger harmful effects that aren’t noticeable until after birth—when it’s already too late to reverse them.

Calls for Scrutiny

Egli stresses that embryo editing is still far from being ready for the clinic. “These base editors—they can have damaging effects on the embryo. So why would you use it if you don’t fully understand that?” he told Nature.

His team is now working to reduce mosaicism and plans to test the technology in embryos that have developed to roughly 100 cells. This is when fertility clinics typically evaluate and freeze embryos.

Speaking to The New York Times, fertility expert Paula Amato at Oregon Health & Science University, who was not involved in the work, called the strategy “promising.” Genomics researcher Greg Neely at the University of Sydney in Australia also praised the work: “This will go down in history in a positive way—less reckless, more careful and ethical than previous attempts.”

Others remain deeply skeptical. Critics argue that embryo editing permanently alters the genetic inheritance of future generations, who have no say in the decision. The study’s ties to Nucleus Genomics also raised eyebrows. The company previously drew controversy for developing genetic predictions for traits such as intelligence and height and for its slogan “have your best baby.”

To Kian Sadeghi, CEO and cofounder of Nucleus, embryo editing extends that vision. The technology could help couples carrying mutations who struggle to produce enough unaffected embryos for selection during IVF.

Fyodor Urnov at the University of California, Berkeley, who was not involved in the study, isn’t convinced. IVF clinics already screen embryos for many inherited disorders without altering their DNA. Given the risks, selecting an unaffected embryo is often a safer option than rewriting its genome.

“In practical terms, therefore, this preprint will solely impact the rapidly growing movement of embryo editors for purposes of ‘baby improvement’,” he said.

That movement, once taboo, is gaining steam. Yet the traits most often cited by proponents—height, intelligence, emotional regulation—are shaped by hundreds or even thousands of genes, which scientists still don’t fully understand. Such enhancements are far beyond the reach of today’s technology. Every additional edit also increases the chance of unintended consequences.

For Egli, that’s precisely why the research should be discussed openly. “Research is necessary to provide information to discourage the wrong use of a technology,” he said.

The post Precise Gene Editing in Early Human Embryos Reignites the ‘Designer Baby’ Debate appeared first on SingularityHub.

Artificial intelligence (AI) is both a technology story and a policy challenge. Governments across sectors and regions are grappling with the same question: how to effectively support the safe, trustworthy development and use of AI in ways that align with their countries’ needs?

Whether setting a national AI strategy or designing concrete initiatives to implement it, governments need guidance that meets them where they are. From experience, I can attest that the hardest part is rarely agreeing on principles; it is finding concrete, comparable examples of how others made them work. That is the gap the OECD AI Policy Toolkit closes.

Released yesterday by the OECD under the Global Partnership on Artificial Intelligence (GPAI), the AI Policy Toolkit is the first version of a practical, non-prescriptive guide for policymakers to translate the OECD AI Principles into action—a deliberate shift from defining what good AI policy requires to showing how to build it.

What the Toolkit does

The Toolkit is an interactive, evolving platform to support policymakers throughout the AI policy cycle. It complements OECD.AI’s broader ecosystem of tools for data, analysis and AI governance.

The Toolkit helps governments target and prioritise where to act. Through AI-powered semantic search, it surfaces relevant policy examples and guidance drawn from real-world practice, turning the OECD’s accumulated evidence into options a policymaker can use the same day—rather than a library to be read.

Built with policy-makers, not just for them

A year ago, the 2025 OECD Ministerial Council Meeting set this work in motion. What followed was less a drafting exercise than a year of listening—and the Toolkit released today reflects what countries told us they needed.

Far from being a top-down exercise, the OECD Secretariat developed the Toolkit with end-users through co-creation across regions. Targeted interviews and four co-creation workshops across Southeast Asia, Latin America and Africa—one of which Costa Rica was proud to host—brought policymakers, industry and experts together to shape its design around how governments actually work and make decisions.

Not only did these co-creation workshops highlight both shared challenges and region-specific priorities. They grounded the Toolkit in fundamental policy questions:

• How to navigate trade-offs between local and global AI models, or between innovation and regulation?
• How to address infrastructure gaps, such as AI compute capacity?
• How to scale AI in agriculture, education or healthcare?

Two lessons that shaped the Toolkit

Moreover, the collaborative approach to developing the Toolkit has yielded important collective lessons.

• First, context is decisive: AI policy must reflect national needs and preferences, institutional capacity and levels of digital maturity.
• Second, addressing shared global challenges such as managing risks posed by advanced AI systems or ensuring diverse linguistic and cultural representation in AI models requires international cooperation as well as tailored policy responses.

Our sincere thanks go to the governments and organisations that, alongside Costa Rica, made this possible—notably Italy, France, Korea, Japan, the United Kingdom, the European Union, the French Development Agency and the Inter-American Development Bank—and to the policymakers and experts who contributed their time and insight. I also commend the OECD Secretariat for its sustained work.

What comes next

The OECD Ministerial Council Meeting (MCM) marks the Toolkit’s first release, which is an important milestone, but it is far from the finish line.

As AI technologies and related policy issues develop, the OECD remains dedicated to ensuring the Toolkit stays relevant through regular updates by:

• Refining and improving the Toolkit through ongoing feedback and iteration
• Incorporating more policy examples and use cases to strengthen its practical relevance via the OECD.AI Policy Navigator
• Expanding its coverage of emerging policy issues, including sector-specific guidance, infrastructure and regulatory approaches

From shared principles to shared practice

The OECD AI Policy Toolkit results from a collaborative effort to transform AI principles into implementation. By integrating OECD standards with regional insights, it guides policymakers in leveraging AI’s opportunities while responsibly and effectively managing its challenges.

The Toolkit’s success will be measured not by its launch but by the policies it helps shape. Its impact depends on sustained collaboration and support. A year from now, I expect us to point to concrete cases where this tool moved a country from principle to practice—better AI policies for better lives.

The post The OECD AI Policy Toolkit: Better AI policies for better lives appeared first on OECD.AI.

Across major economies, trustworthy artificial intelligence is rapidly moving from high-level policy to deployment in core industries such as health, manufacturing and mobility. The European Union is positioning itself for this shift by focusing not only on innovation capacity but also on trustworthy and coordinated implementation across its Member States. Gaining a deeper understanding of where AI is already being applied and gathering evidence on determinants of adoption are essential to assess Europe’s competitiveness and policy readiness.

The European Union is pursuing its ambition to become a global leader in trustworthy AI, moving from high-level policy to on-the-ground implementation. The OECD worked closely with the European AI Office to monitor efforts to develop trustworthy AI and promote its development across the European economy, with a two-volume publication series analysing how this transition is taking place in practice. The first volume focuses primarily on national strategies, initiatives and governance mechanisms for AI in EU Member States. The second, Progress in Implementing the European Union Coordinated Plan on Artificial Intelligence (Volume 2), shifts the lens to sector-specific impact.

The report supports efforts by the European Commission and EU Member States to promote the development, deployment, and use of AI technologies across priority sectors. It draws on extensive multi-stakeholder engagement, including semi-structured interviews with industry experts and insights from dedicated stakeholder workshops. It focuses on concrete use cases that address specific needs in agriculture, healthcare, manufacturing and mobility, selected high-impact sectors where AI can contribute to digitalisation, sustainability and economic resilience. These sectors are most prominently featured across national AI strategies (Figure 1) as priority sectors for AI applications.

Agriculture: from precision to sustainability

Globally, agricultural producers are increasingly turning to AI-enabled precision tools to address labour shortages, environmental pressures and resource constraints. Within Europe, similar dynamics are shaping experimentation with AI-supported farming systems aligned with environmental targets under the European Green Deal.

As AI-driven solutions help optimise resources, reduce chemical inputs and maintain yields, the EU’s agricultural sector is exploring AI deployment to address structural workforce shortages and sustainability requirements. AI-powered agricultural robots and crop and soil monitoring systems are playing a growing role in improving resource efficiency.

Robs4Crops, for instance, illustrates how computer vision and sensor-based systems can enable autonomous mechanical weeding and spraying in vineyards, crop fields and apple orchards. AI4SoilHealth, in turn, is developing an open-access, AI-driven digital infrastructure to help assess and monitor soil health metrics across Europe.

At the same time, many initiatives remain at pilot or experimental stages. Limited digital infrastructure in rural areas, fragmented and inaccessible datasets (due to the resources required to collect high-quality, diverse data across crops, soil, and livestock, and to limited interoperability of existing public datasets), financial barriers, and uncertainty over return on investment continue to constrain large-scale adoption.

Healthcare: enhancing diagnostics and operations

Health systems worldwide are using AI to improve diagnostic accuracy and manage increasing service demand. In Europe, demographic ageing and workforce shortages are strengthening the case for deploying AI across both clinical and operational settings.

AI can help address rising costs and workforce shortages in healthcare while improving patient outcomes through faster and more accurate diagnostics.

One of the most impactful use cases is AI-enhanced medical imaging for the early detection of conditions such as cancer, supported by initiatives including the European Cancer Imaging Initiative. Similar approaches are already being deployed in the United States and Japan, where AI-assisted radiology is helping reduce diagnostic backlogs, highlighting the strategic importance of scaling comparable capabilities across Europe.

Beyond clinical care, the report explores how AI is improving hospital operations. Predictive and optimisation AI systems can help forecast patient inflows and manage bed occupancy, helping healthcare providers reduce staff pressure and waiting times. Perplex, an EU-funded initiative, illustrates how AI can help automate and optimise scheduling and resource management in the outpatient department of a hospital in Madrid.

Despite this potential, barriers such as fragmented health data environments and trust challenges remain significant constraints.

Manufacturing: the rise of industrial intelligence

Competitiveness in the manufacturing sector increasingly depends on integrating AI into production systems, supply chains, and quality control processes. While other major economies are accelerating investment in smart factories, adoption across Europe remains uneven.

AI adoption in EU manufacturing remains modest and highly fragmented, with pharmaceuticals and electronics leading the way, while traditional industries such as textiles and food processing progress more slowly.

Despite these differences, there are areas where AI could have a substantial impact. The report highlights three priority use cases: predictive maintenance, quality assurance and control, and supply chain optimisation.

Predictive maintenance systems, such as those developed through the Made in Europe Partnership, analyse sensor data to forecast equipment failures and reduce costly downtime. In quality control, AI-powered inspection improves efficiency by identifying defects in real time. Comparable smart-manufacturing deployments in East Asia and the United States demonstrate how scaling such applications can strengthen productivity growth and industrial resilience.

Mobility: navigating toward a connected future

Transport systems are becoming increasingly data-driven as cities and logistics operators deploy AI to improve safety, efficiency and sustainability. Across Europe, mobility-sector deployment is closely linked to broader digital and climate transition strategies.

AI can help transport and mobility systems become safer, more efficient and more sustainable. AI-enabled traffic management systems, such as those explored in the AI4Cities project, can reduce congestion by dynamically adjusting traffic-light patterns. Automated driving technologies and intelligent freight logistics systems can further optimise routes and scheduling efficiency. Here, the EU Connected, Cooperative and Automated Mobility (CCAM) Partnership aims to accelerate the transition from research prototypes to real-world applications.

These developments are intended to align with the Sustainable and Smart Mobility Strategy, although gaps in infrastructure readiness and investment capacity remain important constraints for many operators.

Overcoming barriers to scale

Progress in Implementing the European Union Coordinated Plan on Artificial Intelligence (Volume 2) demonstrates significant sectoral potential for AI deployment, while identifying persistent bottlenecks that continue to slow implementation. Addressing these constraints will be critical to moving from experimentation with pilots to widespread deployment that fundamentally transforms the European economy for the better. To do so, the report puts forward a number of key recommendations, including the following:

• Focus on concrete sector-specific AI use cases

Targeted policies, investment, and collaboration will be essential to unlock AI’s full potential in key sectors of the EU’s economy. Public-private-academic partnerships, open innovation platforms, and cross-border collaborations can accelerate AI development and adoption, particularly when grounded in sector-specific needs. Focusing on concrete AI use cases, building ownership and trust through transparency and co-design with end-users, and demonstrating tangible benefits will be key to ensuring that AI strengthens Europe’s economic competitiveness, sustainability, and societal well-being.

• Strengthen data foundations

Investing in high-quality datasets, common standards and shared governance frameworks can enable secure, privacy-preserving data sharing across borders and sectors. Improving data representativeness and reducing fragmentation will lower entry barriers and support downstream AI adoption.

• Expand infrastructure and compute capacity
  Investments in broadband connectivity, cloud and edge computing, 5G networks and AI compute environments—including AI factories and high-performance computing centres—are essential to bridging regional gaps. Initiatives such as EuroHPC are helping ensure that economic actors, including SMEs, can access the computational resources required to train and deploy advanced AI models.

• Close the skills and talent gap
  Unlike their larger counterparts, who tend to have more resources at their disposal, smaller firms and public organisations require access to technical expertise and sector-specific training before large-scale deployment of AI becomes feasible. European Digital Innovation Hubs (EDIHs) are supporting this process through a “test before invest” approach that lowers adoption risks.

• Enhance trust and regulatory coordination
  Regulatory sandboxes allow firms to test innovative AI applications under supervisory conditions, enabling regulatory learning and improving compliance readiness before market entry. Providing clearer guidance and harmonising regulatory interpretation across Member States will remain particularly important for start-ups and SMEs operating under the EU AI Act, alongside relevant existing rules such as the GDPR.

Many of the report’s findings align with the European Commission’s Apply AI Strategy, which focuses strongly on accelerating adoption and the active deployment of AI across the economy. The OECD and the European Commission will continue working together to support implementation of the Strategy and to ensure that lessons from European AI deployment experiences contribute to the broader global AI policy community.

The authors would like to thank John Leo Tarver for his contributions to this report series and blog posts.

The post The European Union is deploying AI across strategic sectors   appeared first on OECD.AI.

In countries around the world, the public sector must ensure the trustworthiness of any algorithmic tools it wants to deploy by verifying that they function as intended, ensuring fair and acceptable use, and guaranteeing explainability of outputs.

Still, numerous high-profile incidents have emerged in which failing to consider one or more of these factors has led to undesirable events or outcomes in areas such as educational qualifications, social security, and debt. The truth is that the widespread use of AI is still new and much remains to be done to standardise approaches to safe deployment.

Nobody notices infrastructure until it fails

This is a common saying in the public sector. To this end, much of the important work in AI governance is routine day-to-day processes, guidance documentation, and activities within organisations that lead to the safety and responsible use critical for trustworthy AI.

The Algorithmic Transparency Recording Standard (ATRS) fits this description. It is a UK government initiative that establishes a standardised way for public sector organisations to publish information about how and why they use algorithmic tools.

In 2024, GPAI ran a project on Algorithmic transparency in the public sector, led by Juan David Gutierrez from Universidad de los Andes in Colombia and supported by CEIMIA (Centre d’Expertise International de Montréal en Intelligence Artificielle) – one of the three Centres of the GPAI Expert Community. The study reviewed global best practices and featured three case studies from Chile, the European Union and the UK. At its core, it explored why countries pursue such initiatives and how championing transparency can help avoid controversies and improve public trust. Before diving into the details of the standard, it is worth looking at a few cases that illustrate why such a standard is necessary.

A ‘mutant algorithm’, or just opaque?    

In the UK, one of the most high-profile controversies occurred in 2020, involving a school exam grading algorithm that estimated grades for students who did not sit formal exams due to COVID. The algorithm was subsequently found to unfairly benefit private school students while limiting test scores from publicly funded schools.  There are also several examples of opaque uses of algorithms within benefits systems. Australia’s ‘Robodebt’ scheme assessment programme fell under scrutiny for generating false debts, resulting in significant impacts on affected individuals. In Denmark, algorithms used by the country’s welfare agency have been the subject of reports of potential mass surveillance, discrimination and social scoring.

Beyond governments, equally high-profile cases have involved algorithms used in hiring systems or credit scoring that discriminated against people based on their gender, race or ethnicity.

Many of these controversies were exacerbated by the opacity of the algorithms used: certain impacts could have been reduced by proactively sharing information about tools and by working with the public and civil society during testing, development and implementation to identify risks ahead of deployment. The tools’ developers would have had the opportunity to engage with comprehensive information in the public domain, rather than relying on incorrect or incomplete information. This is all essential to ensure our emerging ‘algorithmic infrastructure’ stays ‘routine’, behind the scenes, and working as intended.

To address this, the UK government published the ATRS in November 2021. In a nutshell, ATRS provides a structured template and public repository to improve transparency, accountability and public trust by documenting how algorithmic tools work, their purpose, and their impact on decisions that affect citizens. In 2025, reporting the use of algorithms via the ATRS became mandatory for central government departments and Arms-Length Bodies (a specific classification of public bodies in the UK).

To build on the momentum, the government committed to the Roadmap for Modern Digital Government to compile and publish records of all identified in-scope algorithmic tools (as of March 2025) in government departments (excluding their associated public bodies) by the end of 2025. This was achieved, and at the time of writing, 125 ATRS records have been published, with more in progress.

International engagement and CEIMIA initiatives to improve ATRS

The Standard received international attention, with the OECD identifying it as a world-leading initiative and featuring it on the Observatory of Public Sector Innovation. In Europe, the Estonian government translated the Standard and piloted it as part of the UK-Estonia Tech Partnership, providing insights into how the Standard can be implemented across different jurisdictions.

Following the 2024 GPAI project on algorithmic transparency, the UK government’s Department for Science, Innovation and Technology (DSIT) entered into a partnership with CEIMIA under the brand of the Centres of the GPAI Expert Community to review the existing UK transparency standard and obtain rapid feedback as the standard continues to develop.  ATRS also benefits from input from a group of international experts, many of whom participated in the initial 2024 GPAI project. The results informed a set of recommendations, which the UK government is currently considering for a future update.

Testing the Standard through international partnerships, such as the one with Estonia and the Centres of the GPAI Expert Community, is a way for the UK to share best practices, a cornerstone of driving responsible data and AI practices globally.

Transparency and security must work together

Responsibility in a public technology context is often about striking a balance, which can require difficult trade-offs. Transparency matters, but so does security, especially in today’s geopolitically unstable environment.

How algorithms interact with and shape public life remains a major focus worldwide. One of the key themes at the India AI Impact Summit 2026 was Safe and Trusted AI, under which transparency was a specific concern, and the G7 could discuss it as a critical issue.

Transparency is essential for governments adopting algorithmic tools to enhance productivity and growth. Ensuring that it is a priority for public-sector organisations is an ongoing learning process. As the ATRS Standard gains wider recognition and adoption in the UK and beyond, DSIT continues to explore ways to improve it. Part of this involves researching how public-sector teams interact with the ATRS process and balancing security and safety considerations, including those related to cyber threats. 

In the end, all of this helps DSIT to create a healthy balance between maximising transparency – protecting citizens – and ensuring that digital government services remain safe and secure.

Get in touch!

Governments can contact the GPAI Centres of the GPAI Expert Community directly to receive help with algorithmic transparency: contact info@ceimia.org

The post Designing transparency for government AI: Insights from the UK’s Algorithmic Transparency Recording Standard initiative appeared first on OECD.AI.

The AI data paradox

As our daily activities become more digitised, from ordering dinner to receiving medical care, the amount of data produced by humans and machines continues to grow each year.  The internet provides a seemingly limitless flow of accessible data of all formats and natures, from news sites to social media. In early 2026, the internet archive initiative CommonCrawl boasted over 300 billion webpages in its database. Adding to this digital abundance, even larger volumes of data remain underused and locked in organisations’ private databases. Meanwhile, IBM reports that in 2024, the biggest challenge for AI developers was a shortage of high-quality data. This is the AI data paradox: despite an abundance of data globally, AI developers face a scarcity of usable data, with growing expert concerns about a looming data crunch as reported by the OECD (2025).

The new GPAI-associated report, From scraping to ethical data sharing, produced under the VIADUCT initiative, addresses this paradox. Based on 25 interviews and two multistakeholder workshops held in 2025, the report complements the OECD’s Recommendations on Enhancing Access to and Sharing of Data (EASD, 2019) and grounds its analysis in the concrete challenges faced by both data holders and AI developers when sharing and accessing data.

Scraped internet data as a key source for AI training

Data is the cornerstone of modern AI development, from model training to grounding. As online public content continues to grow, it has become a major source of training data for AI models. Initiatives such as CommonCrawl and LAION have harvested, or “scraped”, billions of online pages and images, ranging from news articles and government websites to blogs and social media. These datasets fuel rapid advances in AI tools, but also pose deep challenges.

Figure 1: The AI data value chain: From scraping data for reuse to content to generation

Contemporary AI requires contemporary data sourcing methods

As investment and revenue flow, AI has become a major industry, but data-sourcing methods have not kept pace. Many current AI processes still rely heavily on scraping large amounts of public content, often without permission, proper compensation or quality controls. But this “grab what you can” approach has limits. As the report documents, over 50 copyright and data protection lawsuits have been filed worldwide, while websites are increasingly deploying technical and contractual barriers to prevent scraping. At the same time, the web is increasingly saturated with low-quality material, including AI-generated “slop” and disinformation. Meanwhile, vast amounts of valuable data remain locked away on private servers because concerns about legal risk and confidentiality inhibit sharing.

These quality challenges and legal pressures point to the need for a new approach, one that moves beyond extraction towards mutually beneficial data-sharing arrangements, including commercial and non-commercial agreements as described in the OECD’s Mapping Relevant Data Collection Mechanisms for AI Training report (2025). Instead of relying on a single technical solution, the VIADUCT report presents data sourcing as a systemic challenge at the intersection of law, economics and technology. While data infrastructures have expanded significantly, experience shows that technical capacity alone does not initiate data flows.

Data sharing as a transaction

If not a simple engineering problem, then what is data sharing? At its most basic, data sharing is a transaction between two parties.

For common assets such as treasury bonds, Brent crude oil or soda cans, industry standards, regulations and institutions support transactions and build market trust. By contrast, data is not a standard asset. It is a collection of diverse assets that take different forms, with varying stakeholders and regulatory requirements. Consider two datasets: one with hospital patients’ X-rays, and another with archived news articles. The first dataset contains sensitive personal information protected under the GDPR in Europe and must comply with the principles of confidentiality and consent. News articles in the second dataset are copyrighted works whose owners can prohibit reproduction and opt out of AI training under EU law. These simple examples show that sharing data cannot rely on technology alone but must also consider a dataset’s economic, legal and social contexts and constraints.

Data is not monolithic

Government officials and business leaders often use metaphors like “raw material”, “new oil” and “gold” to describe data. However, contrary to what these metaphors suggest, data is not a uniform resource. In its EASD in the age of AI (2025), the OECD describes data as “recorded information in structured or unstructured formats, including texts, images, sound, and video”, which may also include AI models themselves when training data is memorised. On top of its diversity of shapes and formats, data is also a mosaic of digital assets, each governed by different rules, logic and constraints. Is one dealing with a copyrighted news article? Personal health records? A company’s trade secrets? Government statistics? Open source software code? Understanding this is essential to designing sustainable AI data ecosystems. Each type of data has its own legal guardrails, economic dynamics and technical challenges.

The VIADUCT report classifies data into five governance regimes according to the EU legal framework:

Figure 2: Data governance regimes under EU law

Governance model	Data scope	Dataset example
Copyrighted content	All creative works, including texts, images, videos, sounds, software source code and certain databases. Authors have exclusive rights to reproduce, communicate and distribute their works. They can also opt-out of commercial AI processing.	News article dataset, book archive, social media posts, music database
Personal data	Any data or information relating to an identified or identifiable individual (“data subject”). Under EU’s GDPR, a data processing, such as AI training, must be transparent, confidential, minimal and motivated by legal grounds such as data subject consent, or controller’s legitimate interest.	Customer history database, patients’ medical records, mobile phone GPS locations
Trade secrets	A dataset which is secret, is safeguarded and holds value due to its secrecy. Companies are protected against unlawful access, and can share trade secrets with third parties under strict safeguarding measures.	Engineering blueprint files, pharmaceutical molecule database, commercial lead database
Public sector data	Under EU law, public sector bodies must publish their documents for commercial and non-commercial reuse, free from exclusive license and without fee beyond cost compensation.	Texts of law, national statistics, national company registry
Open data	Any data which can be accessed, shared and used by anyone for any purpose, free of charge. In Europe, this may include contents with expired copyrights, government documents, and open-license content	19th century books, Wikipedia articles, open-source software code

Three guiding principles for sustainable and responsible approaches to data sharing for AI

In this diverse and complex environment, the report sets out three clear guiding principles that apply to all data types and governance regimes to foster sustainable and ethical data sharing for AI.

Legal compliance is the first principle. Data sharing and downstream use must respect applicable legal frameworks. In Europe, this includes honouring copyright holders’ rights, establishing legal grounds for the use of personal data and protecting confidential trade secrets.

The second is trust. Data holders need assurance that their data will be used only for permitted or legitimate purposes and protected by robust cybersecurity measures. Data consumers, in turn, need confidence that datasets are legitimate, accurate and of high quality.

The third principle is fairness. Data-sharing arrangements should be mutually beneficial, crediting organisations that support AI development and, where appropriate, sharing the benefits.

Figure 3: Ethical data sharing principles and best practices

Constraints for ethical data sharing

These principles sound simple, but applying them in practice is challenging. Exchanging large volumes of data requires expertise, which is often lacking in smaller organisations, and the process is fraught with technical, legal, and economic frictions. Data quality issues are a major source of friction, as errors, inappropriate content or bias can create risks and costs for downstream AI use. Ensuring data holders maintain control of their data after sharing or publishing is essential. This is referred to as digital self-determination. This is the case for personal data, copyrighted content or trade secrets, for which the data holder must often grant permission for each new processing act. EU law imposes strict confidentiality standards for personal data, trade secrets and sensitive government data. Finally, to incentivise data sharing and ensure its sustainability for data holders, implementing appropriate economic models such as cost and value compensation appears to be an essential solution.

Figure 4:  Ethical data sharing constraints

Transitioning to a multidisciplinary and implementation-focused approach

This non-exhaustive list of challenges illustrates the limits of relying only on normative or technical responses to address data-sharing constraints. The report investigates alternative approaches, such as opt-out methods, smart contracts, data attribution and privacy-enhancing technologies.

When it comes to ethical data sharing, moving from Is it possible? to How can we implement it? requires developing actionable tools and recommendations to address challenges. Given their diverse and context-sensitive nature, VIADUCT facilitates dialogue among data holders, AI developers, policymakers and researchers on concrete data-sharing projects and experiments. Ultimately, this exercise seeks to create a better understanding of the challenges for data sharing and promote and test innovative solutions at the crossroads of technology, economics and law.

The post Rethinking AI data: From scraping to sustainable and ethical data sharing appeared first on OECD.AI.

Participatory AI initiatives are meant to bring together diverse stakeholders to design and oversee AI systems that are fair and trustworthy. However, a recent review of 80 participatory AI initiatives revealed that, rather than providing participants with genuine decision-making power, the vast majority consult specific communities on narrow implementation details. 

Meanwhile, every major AI lab now conducts some form of public consultation, the EU AI Act requires stakeholder involvement, and the OECD AI Principles regard stakeholder engagement as a fundamental element of trustworthy AI. Participation in AI governance has become more popular than ever. But an uncomfortable pattern is emerging: the more we discuss participation, the less we discuss meaningful engagement, real influence and power.

Many participatory approaches primarily involve communities during the early stages of the AI system lifecycle: design, data collection, and model development, while later stages, such as deployment, monitoring and system evolution, attract far less attention. During my fieldwork across Kenya, Malawi, and the Philippines, a troubling question keeps surfacing: once the participatory design phase concludes, who truly governs the AI system? Even highly participatory processes often dissolve once the system is launched. Governance then shifts back to those who built or commissioned the system. Communities involved in shaping the design often have little influence over how systems evolve to meet ongoing community needs or how they expand beyond their initial scope.

The old lesson we keep relearning

Back in 1969, urban planner Sherry Arnstein published a deceptively simple insight: not all participation redistributes power. Her “ladder of citizen participation” described eight levels, from manipulation at the bottom to citizen control at the top. She called the middle rungs “tokenism”: processes that perform inclusion without actually transferring authority. Arnstein was writing about urban planning in American cities, but her framework resonates powerfully in today’s AI landscape.

Over fifty years later, AI researchers are revisiting this lesson. Recent studies have introduced the concept of “participation washing”: the act of claiming inclusion without the necessary redistribution of power. Other researchers have documented how participatory rhetoric often conceals the ongoing centralisation of decision-making authority.

These findings paint a consistent picture. Participatory AI, despite methodological progress, largely remains at Arnstein’s “consultation” and “informing” levels rather than reaching the “partnership” or “delegated power” levels. The reasons for this are understandable. Genuine participation is costly, slow, and fosters accountability relationships that complicate rapid development and deployment cycles. Organisations optimising for scale naturally minimise governance complexity. However, this means participatory AI often reproduces the very power imbalances it seeks to address.

The real divide: Methods versus infrastructure

Despite all this, the field has made genuine progress in developing methods to meaningfully involve people in AI design. The repository of tools and metrics on the OECD.AI Policy Observatory showcases participatory frameworks that now guide humanitarian and public sector AI initiatives globally. Methods have become increasingly sophisticated, moving beyond superficial consultation towards authentic co-design. What remains underdeveloped is the infrastructure for ongoing governance after deployment.

Think of it this way. Methods answer: “How do we involve people in design?” Infrastructure addresses: “How do people exercise authority after deployment?”

This distinction matters because AI systems, as the OECD definition emphasises, are adaptive. They are never “finished.” They evolve continuously through new training data, model updates, and deployment expansions. Each evolution requires governance decisions.

AI also relies on collective data. These are not tools that individuals choose to use; rather, they are infrastructure that processes communal information and makes decisions that affect entire populations. Without proper governance, participation during the design phase can inadvertently legitimise systems that centralise power. “We consulted the community” becomes a justification for deployment, even if communities no longer hold any authority over the system’s future.

What commons governance looks like in practice

If the challenge is institutional, what institutional forms could address it? One promising approach draws on principles from natural resource commons. Economist Elinor Ostrom received the Nobel Prize for demonstrating that communities can effectively manage shared resources such as fisheries, forests and irrigation systems. The application of commons governance to knowledge and digital resources has been extensively developed in later work, from Hess and Ostrom’s study of knowledge commons to comprehensive frameworks for analysing knowledge commons governance across various institutional contexts.

Commons governance models have several features that matter for AI: collective ownership, where communities hold rights to the resource; participatory decision making, where rules for usage and development are established through community processes; value sharing, where benefits are returned to the community; and continuous stewardship, where governance continues as long as the system operates.

The work I contribute to involves exploring whether these principles can guide AI system development in resource-limited settings. In Malawi, our research team at NYU and our local partners are creating a voice-based crisis reporting system designed to ensure that community governance councils oversee data practices, model updates, and deployment decisions. In the Philippines, we are collaborating with Kalinga State University on an Indigenous Knowledge Data Collaborative that allows communities to control how their traditional knowledge is digitised and used. The CARE Principles for Indigenous Data Governance, emphasising Collective benefit, Authority to control, Responsibility, and Ethics, offer a powerful framework here. These principles emerged from broader movements around Indigenous data sovereignty and represent a significant way to reshape data governance around community authority rather than external oversight.

While the OECD AI Principles mention data trusts as a mechanism for ethical data sharing, commons models take this logic further. Data trusts typically delegate authority to trustees acting on a community’s behalf. Commons models place community decision-making at the centre. The community does not transfer control to a benevolent intermediary; it retains authority itself.

Initiatives like Mozilla Common Voice and various Indigenous data-sovereignty movements are exploring this path worldwide, yet critical questions persist. Can commons governance operate effectively in resource-limited humanitarian settings? What occurs when commons governance moves too slowly for operational needs? Our fieldwork aims to address these exact questions.

What we are learning and what presents challenges

I want to be frank about the limitations of this work. These are early-stage experiments, not established models. However, they reveal important tensions that the wider field needs to address. If participatory AI governance is to go beyond words, we must be honest about what makes it challenging.

Power redistribution is costly. Governance meetings involve travel expenses, translation services, and participant compensation. In our work in Malawi, these costs are significant and surpass typical AI development budgets. However, this should be viewed not just as an expense but as an investment in risk mitigation. Without this investment, systems in complex environments risk rejection, non-adoption and even obsolescence.

Democracy is slow, and crises are urgent. Emergency responses demand quick decisions. Community governance takes time. The challenge is to differentiate between decisions that truly require rapid centralised action and those that only seem urgent to technical implementers.

“Community” is not uniform. Gender, age, and ethnicity influence who takes part and whose voice carries weight, and commons governance does not automatically resolve representation issues. However, it does make them visible and demands specific mechanisms to address them.

Sustainability remains uncertain because meaningful participation requires ongoing resources; governance cannot be an afterthought funded by short-term grants. It must be incorporated into budgets from the outset and treated as operational expenditure. International development finance models will need to adapt to support this. A system that works brilliantly for two years and then collapses because governance funding runs out is not a success.

What this means for AI policy

These experiments demonstrate methods to strengthen international frameworks for stakeholder engagement.

First, we need clearer distinctions between engagement levels. Consultative engagement involves gathering input to inform others’ decisions. Governance authority means stakeholders exercise binding power over system operation. Frameworks often conflate these two very different concepts. When a government or company says it has “engaged stakeholders,” does that mean it sought feedback or that it shared power? Making this distinction explicit helps implementers understand what they are truly committing to, and helps communities know what to expect.

Second, resource models need to change. Governance infrastructure, including ongoing meetings, capacity building, and conflict resolution, should be seen as a way to protect assets rather than as overhead. Current assessments of AI pilots rarely differentiate between technical and governance factors when a project fails. This complicates the process of allocating resources effectively. Funding structures must recognise that governance is the mechanism that maintains an AI system’s viability and trust over time.

Third, we need governance metrics. The OECD effectively monitors AI policy implementation. But we also need ways to measure the quality of power distribution. Who makes binding decisions about how a system evolves? Just as “technical debt” builds up when code is rushed, “governance debt” accumulates when engagement is overlooked. Eventually, the interest is paid in the form of lost trust or system failure. Recent work on AI accountability frameworks suggests promising directions for creating such assessments.

Finally, community data ownership has an unclear legal status in most jurisdictions. Cross-jurisdictional research on legal frameworks that support collective governance would be very valuable. Experiments such as New Zealand’s Māori data sovereignty frameworks and Barcelona’s data cooperatives offer promising models to learn from. The goal is not to impose universal frameworks but to document what works, what does not, and where the legal gaps are most severe.

Community governance authority as infrastructure

The participatory turn in AI governance represents genuine progress. However, involving people during design without engaging them in governance risks becoming a sophisticated form of consultation that maintains existing power structures. This is exactly what Arnstein warned against more than fifty years ago.

Moving from participation to power involves treating community governance authority as infrastructure: something that requires investment, upkeep, and institutional backing comparable to the technical systems it oversees. It means funding that supports governance alongside technical development. It means metrics that evaluate power distribution, not just engagement processes. And it means honest recognition that meaningful participation is slower and more costly than consultation, but also more likely to produce systems that communities genuinely trust and use over the long term.

Eighty participatory AI initiatives were reviewed, and most of them never moved beyond consultation. That finding should concern anyone who values participation. If we are serious about closing the gap between engagement and authority, we must start building the governance infrastructures to do it. AI remains in its early stages, and there is much we still need to understand. But one thing is becoming clear: if AI is considered infrastructure, it requires governance infrastructure. Otherwise, there is no trustworthy AI.

The international community has made significant progress in defining what responsible AI looks like. The next step is investing in the unglamorous, difficult, and necessary work of establishing governance structures to uphold these standards. This involves supporting communities not only as participants in design but also as equal partners in decision-making.

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Among the various tools available to policymakers, regulatory sandboxes have gained considerable prominence in the AI governance landscape because they enable supervised innovation testing under controlled conditions and within limited timeframes. This can help to identify risks early, foster regulatory learning and refine regulatory requirements before they are applied at scale.

As AI regulatory sandboxes expand across jurisdictions and sectors, common design principles, recurring challenges and opportunities for greater effectiveness and policy coherence are emerging. As this happens, institutional co-operation and knowledge sharing are more important for ensuring coherent and effective regulatory experimentation both nationally and across borders.

In November 2025, the OECD webinar “AI Sandboxes: Sharing knowledge for success” brought together government officials, regulators and policy experts from seven countries to discuss the design and implementation of AI regulatory sandboxes. Here are the event’s key takeaways.

What is an ‘AI regulatory sandbox’?

Although there is no universally accepted definition, a regulatory sandbox generally offers temporary regulatory flexibility or waivers, allowing innovative products, services, or business models to be tested under controlled conditions and regulatory oversight. This approach promotes responsible experimentation and innovation while protecting the public interest.

To cite a few examples, Singapore’s AI healthcare sandbox offers guidelines for synthetic data to minimise privacy risks while allowing realistic testing. In the UK and other countries, AI-powered innovations in financial services are being tested under supervision that helps to prevent consumer harms such as biased scoring and automated decision-making. 

In AI, this approach aligns with the OECD AI Principles – specifically Principle 2.3, which encourages governments to promote experimentation to enable the safe testing and scaling of AI systems. Similarly, the Recommendation of the Council for Agile Regulatory Governance to Harness Innovation urges governments to facilitate greater experimentation, testing, and trialling to stimulate innovation under regulatory supervision.

AI regulatory sandboxes are valuable for testing new technologies and rules in a safe, controlled way before full rollout. They offer less benefit if risks are low or if rules are already well established, but can be useful for compliance and learning in more complex regulatory environments. Decisions to utilise sandboxes should follow clear criteria to ensure efforts are appropriately targeted. Generally, initiatives with high innovation potential, substantial risks, and opportunities for regulatory discovery and improvement (including by removing barriers to beneficial innovation) should be prioritised. Key regulators, industry actors and other relevant stakeholders should be involved in this process.

In July 2023, the OECD published the policy paper Regulatory Sandboxes in artificial intelligence. Building on lessons from fintech, the report highlights the benefits of AI sandboxes, including accelerating market entry, improving regulatory understanding, and stimulating investment. It also explains why adapting the traditional sandbox model to AI presents unique technical and governance challenges. As a cross-sectoral technology, AI covers multiple legal, ethical, and technical domains, requiring strong coordination among several regulatory authorities.

Since the paper’s release, the use of AI sandboxes has accelerated. By February 2025, the Datasphere Initiative identified over 60 sandboxes worldwide related to AI, data, and technology. Furthermore, key regulatory and policy frameworks, including the European Union’s AI Act and America’s AI Action Plan, view regulatory sandboxes as essential tools for fostering AI innovation and ensuring the safe development and adoption of AI.

Insights shared during the webinar by experts from Spain, Thailand, Luxembourg, Brazil, Korea, Israel and Singapore offer valuable lessons on how different jurisdictions design and operate AI sandboxes, highlighting what works, where challenges arise, and how approaches vary across contexts. For example, Spain provides appropriate, tailored guidance to ensure effectiveness and facilitate regulatory compliance further down the line. Brazil’s sequenced approach includes capacity-building to enable participants to contribute to effective experimentation and evaluation.

>> REVISIT THE WEBINAR AND RELATED PUBLICATIONS <<

Six insights about AI regulatory sandboxes from around the globe

1. AI sandboxes are not uniform

• According to the Datasphere Initiative, three primary types of sandboxes are emerging worldwide, especially within the context of AI. Regulatory sandboxes: Collaborative processes where regulators work with innovators to test innovations under regulatory supervision.
• Operational sandboxes: Testing environments and infrastructure where data can be hosted and accessed in controlled conditions.
• Hybrid models: Combining regulatory oversight with operational capabilities, sometimes offering infrastructure and operational spaces for testing and experimentation (e.g., “supercharged sandbox” in the UK).

These models intervene at different phases of the policy and regulatory lifecycle. Some are employed before formal regulation to identify gaps and suggest necessary updates. Others operate during the development process, supporting iterative regulatory design. Some focus on helping understand legal obligations and ensure regulatory compliance, such as under the EU AI Act. Sector-specific sandboxes are also common, with countries adopting different approaches depending on regulatory priorities and institutional settings. Across these models, regulatory waivers are frequently used to enable experimentation under regulatory supervision. 

Several experimentation-related initiatives, such as regulatory testbeds, living labs, or policy prototyping, share certain features and objectives with regulatory sandboxes. What truly distinguishes sandboxes is that they are the most institutionalised form of regulatory experimentation, usually led by regulators and integrated with regulatory supervision.

2. Coordination is essential

AI does not always fit neatly within existing sectoral, jurisdictional, or administrative boundaries. Its development and deployment span multiple regulatory domains, making effective coordination crucial. Luxembourg’s approach demonstrates this well, showing that AI sandboxes are more than testing spaces—they are platforms for regulatory collaboration and coordination. Luxembourg’s model brings together 11 authorities and innovation actors to align priorities and prevent fragmentation, emphasising the need for skilled project management alongside legal and technical expertise. 

Specific stakeholders within the AI ecosystem pursue different objectives: data protection authorities concentrate on privacy, cybersecurity authorities on resilience, and innovators on efficiency and speed. They also offer different kinds of expertise. Sandboxes can offer a neutral space to reconcile these priorities, fostering trust and mutual understanding. To achieve this, managing the expectations of involved parties and clearly defining the objectives of a sandbox are particularly important steps. 

In Thailand, a multi-faceted approach to AI regulatory sandboxing shows how balancing safety and flexibility depends on agile cooperation between sectoral regulators and industry. This approach integrates three complementary pathways: in the short term, fostering AI deployment where existing rules already allow it; in the medium term, establishing sector-specific sandboxes to manage domain-specific risks and opportunities; and eventually, developing system-wide sandboxes, including for government use, to test cross-cutting applications. Together, these mechanisms help promote AI-driven innovation within current legal frameworks while leveraging testing and experimentation to better understand the implications of emerging AI applications. Effective coordination is essential to prevent duplication of effort, regulatory gaps or conflicting rules.

Sandboxes can also play a valuable role in involving expert and academic communities in the development of AI regulation, with countries such as Spain, Luxembourg, and Brazil benefiting from such expertise at multiple stages of sandbox design and operation.

3. From policy to practice, and back again

AI sandboxes are increasingly used to bridge the gap between regulatory frameworks and real-world implementation. For example, Spain’s regulatory sandbox pilot translates the EU AI Act’s requirements for high-risk AI applications into practical compliance steps, enabling early identification of gaps and clarifying obligations for deployers. In December 2025, the Spanish AI Supervision Agency (AESIA) published a series of introductory and technical resources, developed from insights gathered during the regulatory sandbox pilot, that demonstrate how sandboxes can support evidence-based compliance guidance. Luxembourg’s AI sandbox, in turn, acts as a coordination platform to ensure lessons learned on overlapping obligations feed into the domestic operationalisation of the EU AI Act and related future guidance.

In July 2025, Singapore launched its Global AI Assurance Sandbox, building on insights from a previous pilot phase, to create a testing environment where creators or deployers of GenAI applications can have their applications evaluated by expert technical testers. Key risk aspects examined during testing include hallucination, undesirable content, data leakage, and vulnerability to adversarial prompts, with the findings informing policy guidance. Brazil’s Regulatory Sandbox on AI and Data Protection also exemplifies this trend. It aims to promote transparency, privacy by design and responsible innovation in AI systems that handle personal data, using structured experimentation to help innovators achieve regulatory compliance and assist regulators in understanding how rules work in practice and where adjustments may be necessary.

Simultaneously, AI sandboxes continue to shape future regulatory frameworks. In Thailand, sector-specific sandboxes for digital payments, digital assets, banking and insurance are expected to help regulators understand real-world AI applications and prepare for system-wide governance. As sandboxes move regulation from theory to practice and back to policy, they create an iterative loop that can strengthen trust and adaptability in AI governance. To achieve this, sandboxes should generate insights to inform better regulation. This, in turn, requires consistent reporting, sharing of results, and the establishment of feedback loops across sectors and countries to boost compliance and policy development.

Nevertheless, translating sandbox results into regulatory improvements remains challenging, even in countries like Korea, which has considerable experience conducting regulatory experiments across sectors.

4. Incentives matter

Participation in AI sandboxes is not automatic. Clear and well-designed incentives are essential for both innovators and regulators. Israel, for instance, has introduced a government fund that provides financial support, legal counselling and mentorship for regulators launching AI sandboxes, while also offering grants to participating firms. Similarly, Singapore reduces testing-related barriers to GenAI adoption through practical guidance and access to specialised testing partners.

These models recognise a fundamental challenge: AI experimentation is resource-intensive and needs to focus on areas where it matters most. Without targeted support, regulators may struggle to operate sandboxes, and companies might be hesitant to participate. Furthermore, when offering incentives, authorities should encourage a diverse mix of participants—small firms, big players, different sectors, and different AI applications—to ensure that sandbox insights are both representative and robust. The complex nature of regulatory sandboxes themselves may also pose challenges for some applicants or even participants. In this context, Brazil outlined a three-stage execution framework, starting with capacity building for selected participants undertaken by a partner university, before advancing to the experimentation and evaluation phases. 

5. The growing need for interoperability and cross-border collaboration

As AI systems operate across borders, there is a growing need for AI sandboxes to extend beyond national borders. Without international coordination, firms may engage in ‘jurisdiction hopping’, seeking the most permissive regulatory environments. Interoperability between sandboxes is thus becoming a governance necessity. Cross-border collaboration is also crucial for international regulatory cooperation. In Brazil’s case, preparatory work to develop the sandbox included international consultations on the experimental methodology. This approach enables the benefit from international practices and standards and facilitates the sharing of experiences in later stages of the project. 

Cross-border sandboxes have already proven their worth. Singapore’s Global AI Assurance Pilot, for example, involved 17 AI deployers from nine countries collaborating with 16 specialised testers from the US, UK and Europe. Use cases include summarisation, chatbots to AI applications in healthcare, finance and human resource management. These cross-border tests allowed regulators and companies to understand how AI performs in different legal, cultural and technical environments. For example, a chatbot that safely managed English queries inadvertently leaked confidential information when prompted in Mandarin, demonstrating the importance of multilingual testing. 

6. AI sandboxes come with challenges of their own

AI sandboxes face several challenges. Regulators often encounter capacity limitations and lack the technical expertise or project management skills necessary to supervise complex AI systems. Fragmentation and coordination issues also arise, as AI spans multiple sectors and necessitates collaboration among numerous authorities, including across borders. 

Designing suitable requirements and safeguards for sandbox frameworks can be challenging, especially when multiple regulatory regimes are involved, as demonstrated by Brazil. Deciding the appropriate level of transparency, human oversight, and data governance can be particularly difficult when firms seek waivers to speed up testing.

At the same time, Korea’s experience demonstrates that although safety and consumer protection rules are vital, excessively strict requirements may deter participation, especially among SMEs, and hinder experimentation. Safeguards should therefore be proportionate to and aligned with the risks posed by the technology, as overly complex procedures can undermine the agility required in regulatory sandboxes in a rapidly evolving AI landscape.

Measuring the impact of AI sandboxes is also difficult. Without clear metrics, sandboxes risk becoming isolated experiments rather than influential policy tools. Ideally, impact should be monitored across various areas, such as faster time-to-market for compliant AI systems, increased regulatory clarity and coherence (including through less fragmentation), and tangible updates to laws and standards shaped by sandbox insights. 

Furthermore, as highlighted in a 2024 OECD policy paper, there are potential limitations concerning legality, feasibility, resources, and equity. Regulatory experiments should adhere to constitutional norms, including those concerning equal treatment.

A vital element for responsible innovation and public trust?

As a relatively new regulatory tool designed to address a rapidly evolving general-purpose technology, AI sandboxes raise significant questions about their role. Some of the questions raised during the online workshop include:

• What role might civil society play in AI sandboxing?
• How can public institutions build the expertise required to supervise regulatory sandboxes involving frontier-level AI systems?
• How can sandboxes balance flexibility with protecting long-term societal values (e.g., what types of safeguards should be in place regarding regulatory exemptions)?
• What measures, such as reporting and documentation requirements, talent management, and capacity building, are necessary to ensure transparency and build trust in AI regulatory sandboxes? 

As AI governance develops and these questions are addressed, AI sandboxes hold the potential to become key tools for promoting responsible innovation, enhancing governance and building public trust in AI systems across the globe. 

The OECD is well placed to advance these objectives by facilitating the systematic exchange of knowledge and expertise, and by developing standardised, comparable frameworks for measuring outcomes. It can also use its convening role to support alignment on guidance for the targeting, design and implementation of AI regulatory sandboxes. By grounding this work in empirical evidence and practical experience, the OECD can help strengthen the overall evidence base and inform more effective policy approaches.

The authors would like to thank Natalie Cohen, Lucia Russo, Guillermo Hernandez, Xavier Pearson, Viktor Samek and John Leo Tarver for their contributions to this piece.

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AI agents and agentic AI based on large language models are becoming more autonomous and capable of interacting with both physical and virtual environments. As the capabilities of these AI systems grow, they are gaining visibility, and with reason. It is reaching a point where they could become the driving force behind innovation, investment and improved productivity across sectors by streamlining processes and enabling more efficient operations.

While ideas related to agency have long been explored in academic research in fields such as philosophy, economics and computer science, recent advances in AI are stretching conceptual boundaries. As AI’s capabilities evolve, so do our shared understanding of what qualifies as AI agent and agentic AI.

The OECD report, The agentic AI landscape and its conceptual foundations, developed by the OECD.AI Expert Group on Agentic AI, helps clarify what AI agents and agentic AI are and how they differ. Grounded in the OECD AI system definition, the analysis examines how these terms are defined and used across the literature. By analysing key features, overlaps and distinctions and mapping them to the core elements of the OECD definition of an AI system, the report helps to establish more precise and consistent terminology. And in a rapidly evolving field, conceptual precision is essential for effective, well-informed governance.

Three key messages stand out in the report:

• AI agents and agentic AI are closely related, but not interchangeable.
• Agentic AI ought to be seen as a socio-technical paradigm.
• Despite technological gaps and varying levels of maturity in areas such as digital security and privacy, uptake is growing.

The common foundations and meaningful distinctions of AI agents and agentic AI

Our analysis shows that AI agents and agentic AI share foundational characteristics. Both involve systems with a degree of autonomy that pursue goals and can perceive and act within physical and virtual environments.

However, there are differences that mean these terms are not interchangeable.

1. AI agents can be understood as systems that perceive and act on their environment with a degree of autonomy, using tools as needed to achieve specific goals and adapt to changing inputs and contexts.
2. By contrast, agentic AI generally refers to systems composed of multiple co-ordinated AI agents that can break down tasks, collaborate and pursue complex objectives autonomously over extended periods. Agentic AI systems are designed to operate in more open-ended, less predictable physical and virtual environments, and to function with minimal human supervision.

In short, agentic AI is more complex, as it can co-ordinate multiple agents, perform task decomposition and delegation, and sustain operations over longer periods. It can also operate in more complex, less predictable environments with limited human oversight.

Agentic AI as a socio-technical paradigm

Agentic AI systems are not isolated technical artefacts. They are frequently embedded in social contexts and interactions and operate within a socio-technical paradigm.

Their value lies not only in autonomous action, but in interaction with other AI agents, humans and institutional processes. Co-ordination and negotiation across these actors require advanced reasoning capabilities, robust infrastructure and reliable communication protocols.

This relational perspective is an essential part of what agentic AI is. This means that understanding how they interact within broader ecosystems is essential to designing agentic AI systems that function responsibly and effectively, particularly in open or high-stakes environments.

Uptake is accelerating, but maturity is uneven

The report also presents descriptive evidence on trends in AI agent adoption. Many developers have already integrated them into their toolkits, and survey data indicate that nearly half of respondents on Stack Overflow use them or plan to do so.

To be clear, adoption should not be confused with maturity. Developers highlight opportunities to further strengthen the security, privacy and accuracy of AI agents. These concerns underscore an important point: as the capabilities of agentic AI advance rapidly, progress in robust, trustworthy AI systems must keep pace.

A foundation for further analysis

Overall, the report provides a descriptive overview of the agentic AI landscape, clarifying key concepts and characteristics and establishing a shared analytical foundation. By anchoring the discussion in the OECD AI system definition, it aims to promote coherence across technical and policy communities.

Looking ahead, an improved understanding of real-world use will be essential to identify where safeguards, standards, and governance mechanisms will be most effective. Policy-relevant typologies that build upon this work could help guide governance efforts to distinguish systems by level of autonomy, degree of adaptiveness, domain of operation and scale of impact. Evidence-based policymaking will require more empirical data on how AI agents and agentic AI are being adopted and used across sectors, as well as clearer evidence of their broader implications and impacts.

This report contributes to a clearer, shared understanding of agentic AI and provides a basis for thoughtful, forward-looking policy grounded in conceptual clarity. As agentic AI systems become more capable of coordinating multiple AI agents, taking action and operating over longer periods, governance conversations have to keep pace.

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Companies hoping to take advantage of AI’s opportunities need to be trustworthy. Whether investing in, developing, or using AI, the OECD’s new Due Diligence Guidance for Responsible AI provides businesses with an internationally agreed, government-backed tool to demonstrate that markets and societies can trust their AI systems.   

Recent international reporting underscores a growing consensus: AI is not just a technological shift. It is a major geopolitical, economic, and societal phenomenon that demands coordinated action amongst all actors, including companies. 

AI has the potential to transform society through productivity, economic value and solutions to complex challenges, but for these benefits to materialise, AI needs trust.  So far, the technology seems to advance faster than its guardrails. The gap between AI systems and appropriate safeguards is now one of the defining challenges for policymakers and global businesses alike. Both are under pressure to balance AI innovation and diffusion with safety and risk management. Success depends on getting the balance right.

Risks throughout the AI value chain are continually evolving

Risks to people and the environment can manifest at any point along the AI value chain. The OECD actively tracks and categorises risks through its AI Incidents and Hazards Monitor.

Here are a few examples. At one end of the AI value chain, there are the people who label, clean, and moderate the vast datasets required to train AI models. They can face low wages, long hours, and suffer psychological distress from exposure to harmful content. Companies need to ensure decent work for data enrichment workers.

The environmental costs of running AI systems can also be significant, particularly for energy and water consumption by data centres that power AI development and deployment, which may lead to higher energy prices.

Data privacy is another critical concern. AI models are trained on massive datasets that may include personal or sensitive information. If these datasets are not properly anonymised and secured, it can lead to data breaches. If AI models “memorise” and reproduce sensitive data in their outputs, they can expose confidential details, creating legal and ethical dilemmas.

At the other end of the AI value chain, the potential for AI misuse poses risks such as reputational harm and the spread of misinformation. AI-generated deepfakes, for instance, can be used to create realistic but fabricated content, damaging reputations or manipulating public opinion. Similarly, AI can be used to generate and disseminate mis and dis-information at speed and scale, eroding trust in institutions and potentially influencing events.

Worldwide, governments, consumers, and markets are calling for responsible and trustworthy AI. This is one of the reasons for the surge in mandatory and voluntary AI risk management frameworks, responsible AI initiatives, global agreements, academic research and statements from industry leaders and investors. However, this surge in frameworks is also increasing complexity for companies, as risk management is defined differently across jurisdictions and understanding of AI-related risks is evolving.

OECD Due Diligence Guidance for Responsible AI: A flexible, whole-of-value-chain approach to support businesses in navigating evolving risks and rules

This is why the OECD has now developed the first internationally agreed, government-backed Due Diligence Guidance for Responsible AI. Backed by all the OECD’s member countries, plus 17 partner governments and the EU, this Guidance helps enterprises navigate the complex terrain of AI risk management. It is designed to help businesses ensure that the AI systems they develop are trustworthy, used and developed safely and responsibly, and aligned with broad societal values.

Concretely, this Guidance offers:

• A step-by-step framework for enterprises to set up internal management systems capable of proactively identifying and responding to risks related to human rights, labour standards, and environmental impacts.
• Comprehensive coverage of all risk areas from the leading international standards that it is built on and reflects, notably, the OECD Guidelines for Multinational Enterprises on Responsible Business Conduct (MNE Guidelines) and the OECD Recommendation on Artificial Intelligence (AI Principles);
• Recommendations and implementation examples for everyone in the AI value chain, from data suppliers and infrastructure providers to financiers and end-users – including enterprises. The guidance emphasises a “whole-of-value-chain” approach to support secure and resilient AI value chains more resistant to supply chain shocks and interference.
• A roadmap of related provisions in existing frameworks, indicating how each step complements and relates to relevant provisions from AI risk management frameworks. This feature helps enterprises understand how implementing this guidance can help them meet expectations from multiple sources and navigate the current landscape of AI risk management frameworks.

Responsibility and trust can give a competitive edge

Responsibility and innovation not only coexist but also reinforce each other. Companies that show a commitment to responsible AI and actively address potential risks can gain trust from investors, customers, regulators, and policymakers. This trust leads to a competitive edge. Instead of hindering innovation, responsible AI practices can speed up growth by reducing obstacles and preventing costly damage to reputation, legal issues, and society.

Responsible and trustworthy AI is becoming increasingly crucial for accessing global markets as international regulatory and voluntary risk management frameworks evolve. Companies in the AI value chain that meaningfully implement the Guidance’s recommendations can position themselves advantageously for cross-border expansion, potentially avoiding the substantial costs of retrofitting systems to meet various regional requirements.

As AI continues to develop rapidly, frameworks and best practices for responsible AI are likely to evolve as well. To help stakeholders keep pace, the OECD will launch an online navigation tool later this year with updates on new frameworks and use cases.

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Artificial intelligence is changing fast, and the world is feeling both excited and uneasy about it. People use AI tools every day in hospitals, classrooms, companies and public services, yet the rules that guide these tools are still developing. Many governments are trying to find a balance between innovation and safety. Others are trying to make sure that AI actually improves people’s lives without widening gaps.

This is the backdrop against which the India AI Impact Summit 2026 will take place in New Delhi in February. Earlier global AI meetings, including the 2023 gathering at Bletchley Park and subsequent summits in Asia and Europe, helped define the risks and push for action.

These summits did not occur in isolation but are part of broader global efforts to coordinate responsible approaches to AI. The G7 Hiroshima Process in 2023–24 established a shared commitment to trustworthy, human-centric AI, leading to the adoption of the Hiroshima Declaration, which calls for international cooperation on safety, transparency, and risk mitigation.

Building on that, the Paris AI Summit in 2025 moved the conversation toward implementation, with an early agreement on safety evaluations, incident-reporting mechanisms and commitments to support countries with limited technical capacity. The India AI Action Summit represents the next step in this progression: translating these collective principles into measurable on-the-ground outcomes.

In recent months, people have repeatedly asked me two questions. Why should India host such a major global meeting? And is this summit actually useful for India and the world?

The simple answer here is that the next phase of AI will not be decided by a small number of companies or countries. It will depend on whether billions of people, especially in the Global South, can use AI safely, affordably and accountably. India, with its linguistic diversity, strong digital public infrastructure and experience deploying technology at a population scale, is well positioned to help shape this practical phase.

However, AI comes with challenges related to privacy, digital exclusion and the balance between innovation and oversight. But these very tensions make India’s experience pertinent to other countries facing the same trade-offs.

This blog post explains why that matters, what the international community can expect in Delhi, and how we should measure progress at the end of the summit.

Why India, and why now

AI deployment in the Global South will shape global outcomes

Much of the world’s discussion on AI has focused on frontier models, international competition and long-term safety. These debates are important, but AI’s greatest impact will be felt in how it reaches ordinary people. From farmers and students to small businesses, frontline health workers and local governments.

More than half of the world’s population lives in countries categorised as the Global South — a term first popularised in the late 1960s to describe post-colonial economies, and one I don’t fully agree with, as it often flattens diverse countries into a single broad category.

If AI is to be truly global, it must work for multilingual, resource-constrained and diverse environments. This includes reliable translations, culturally grounded datasets, accessible interfaces and low-cost deployments. It also means designing systems that respect human rights and democratic norms even in places with limited regulatory capacity.

India sits at the intersection of these challenges. With over a billion people, 22 official languages and thousands of dialects, any technology deployed at scale must be inclusive by design. India’s experience offers lessons for many other countries navigating the same realities.

India has a strong track record in large-scale digital public infrastructure

India’s digital public infrastructure, or DPI, is one of the most widely referenced success stories of how technology can enable access and accountability. Systems like Aadhaar, UPI and DigiLocker have helped millions access identification, financial services and digital records. These platforms were built with interoperability and openness in mind, which has led to a wave of public and private innovations.

At the same time, these systems have also raised important questions about privacy, data security, and exclusion of marginalised communities who lack documentation or digital access. India’s ongoing work to address these concerns—through data protection legislation, improved grievance mechanisms, and efforts to reach the digitally excluded—provides practical lessons about implementation challenges that other countries will inevitably face.

The India AI Impact Summit is expected to draw on this experience, including both successes and areas for improvement. The global community is watching to see how India will frame the link between AI and digital public goods, and how these tools can be used responsibly in sectors such as education, healthcare and social protection.

International expectations are focusing on implementation leadership

The earlier global AI safety and governance summits created momentum. They helped identify risks, promote transparency and encourage cooperation. But now, many countries and organisations want clarity on what should happen next.

The India summit is an opportunity to shift the conversation from what AI might do to what it should deliver. This includes measurable improvements in public services, clearer accountability mechanisms and more inclusive access to AI tools. By focusing on implementation, India can complement the work of the OECD-GPAI, UNESCO and other international bodies.

 What the India AI Impact Summit should prioritise

A conversation about measurable, real-world outcomes

The summit should begin by asking a straightforward question: What changes on the ground when AI is deployed responsibly at scale? To answer it, discussions need to move beyond broad aspirations and focus on concrete domains like public healthcare triage, classroom support tools, agricultural advisory systems, and other public-sector applications where impact can be seen and measured.

Government delegates should be encouraged to present evidence, not statements of intent. That means clear baselines, transparent evaluation methods, and metrics that reflect real improvements: higher diagnostic accuracy, increased crop yields and shorter benefit-processing times all achieved without compromising fairness or human oversight.

If the summit succeeds, it will shift the global conversation toward what works, for whom, and under what conditions.

Three ways the Global South can shape the international agenda

A meaningful summit requires a wide range of voices—especially from regions where AI deployment will shape social and economic outcomes for decades to come. Countries across Asia, Africa, Latin America and the Middle East bring their lived experiences of linguistic diversity, data scarcity, affordability constraints and uneven digital access.

The summit should create space for these countries to set priorities rather than simply respond to frameworks developed elsewhere. Their perspectives are vital for building governance models that reflect the realities of low-resource contexts, rather than idealised assumptions from high-income environments.

A more pluralistic conversation would reinforce a simple principle: responsible AI cannot be universal if it is not also contextual.

Rebalancing the narrative with the immediate societal, environmental and institutional challenges

One of the most important roles the summit can play is to broaden the global AI discourse. Today, existential risk narratives dominate many international forums, often overshadowing more immediate and systemic issues. The India AI Impact Summit should refocus attention on the present: AI’s energy footprint, labour displacement, rising misinformation, digital exclusion and the growing pressure on public institutions to oversee algorithmic systems they are not adequately equipped to oversee.

The environmental dimension deserves particular attention. Training and deploying large AI models require significant energy resources, disproportionately affecting the Global South. Many of these countries face climate vulnerability, fragile grids and competing development priorities. For regions already grappling with heatwaves, droughts and energy shortages, the cost of “AI at scale” cannot be separated from broader planetary concerns. If AI is to be deployed responsibly, discussions must also consider energy and natural resource efficiency and equitable access to compute.

These issues determine how people experience AI today and whether they trust it tomorrow. Giving them equal weight would help correct the imbalance in global discussions and lead to governance that addresses risks people actually face, not only those imagined at the far horizon.

Potential wins for the India AI Impact Summit

Practical pathways for responsible public-sector deployment

Across sectors, governments are eager to use AI to strengthen healthcare, expand access to education and streamline welfare delivery. Yet many lack clarity on how to procure, evaluate or oversee these systems responsibly. A meaningful outcome of the summit would be simple, actionable pathways that public agencies can adopt without specialised expertise. These might take the form of evaluation checklists with acceptable error and bias thresholds, procurement templates with human oversight requirements, or clear guidance on when and how officials should override an AI recommendation. Transparent case studies and training for civil servants would also help countries move from hesitation to informed, confident experimentation.

Strengthened mechanisms for trust and accountability

Concerns about misinformation, bias, privacy and security continue to rise, and many countries, particularly those with limited technical capacity, need practical tools to manage these risks. The summit could make a real contribution by advancing shared approaches to incident reporting, auditing and assurance, as well as safety testing methods that work across varied deployment contexts. Small pilot frameworks would help establish a common baseline of accountability. Such efforts would not only support global cooperation but also build public trust at a time when many citizens and policymakers remain uncertain about the reliability of AI systems.

Broader cooperation on multilingual and inclusive AI with robust safety infrastructure

Many countries struggle with adapting AI to their unique linguistic profiles. India’s long-standing work in language technologies positions it to convene collaborations on multilingual and inclusive AI. New partnerships on datasets, dialect-specific models, local-first interfaces and research on linguistic bias could meaningfully expand access for millions of people worldwide.

But inclusion must be matched with safeguards. As AI tools become more widely available, countries will need parallel investments in risk-assessment expertise, regional coordination on harmful content and support for the development of first-generation regulatory frameworks. Striking the right balance between openness and safety would reinforce core OECD AI Principles and help ensure expanded access does not bring greater vulnerability.

Broader implications for global AI policy

Everyday impact before frontier risks

Research on frontier AI risks must continue, but the India AI Impact Summit signals an important rebalancing of global attention, as mentioned before. It asks policymakers to look beyond hypothetical future scenarios to acknowledge how AI is already shaping critical aspects of our daily lives, from healthcare triage and classroom instruction to welfare delivery, agricultural advice and urban mobility.

For most people, the urgent question is not whether AI poses an existential threat, but whether the systems they encounter today are reliable, safe and genuinely useful. The summit’s focus on practical impact aligns global governance with lived reality.

Ensuring AI benefits for everyone

A second implication is the reaffirmation that inclusion is not a downstream concern but a prerequisite for responsible AI. Global conversations often gravitate toward powerful models built in highly resourced environments, yet billions of people rely on limited connectivity, low digital literacy and minority-language interfaces.

India’s leadership places these conditions at the centre of the global agenda. It broadens the imagination of what “good AI” must account for, reminding the world that both equitable deployment and cutting-edge capability are essential to whether AI helps or harms societies.

Building a more open and collaborative ecosystem

The summit also nudges the world toward a more open and cooperative model of AI development. Some countries can share tools, datasets, and governance mechanisms to help each other build their own capabilities rather than remain passive consumers. Openness here is not about lowering standards; it is about raising the global floor and ensuring that safety capacity grows alongside access. Many countries want to participate meaningfully in the AI economy, and the summit offers a platform to explore practical pathways for doing so.

What the world should take away from Delhi

The India AI Impact Summit 2026 is more than just another international meeting because it represents a shift from abstract debates to concrete action. Its core question — how to make AI useful, safe and inclusive at scale — goes to the heart of global governance. If the summit delivers practical tools, clearer deployment pathways and stronger cross-regional collaboration, it will set a new benchmark for what international coordination on AI can achieve.

The world is watching India, not because it claims to have all the answers, but because it has repeatedly demonstrated the ability to turn large-scale ideas into real-world outcomes.  And it has done so while openly confronting the tensions and trade-offs that accompany such efforts. In a period of rapid technological change, this experience is invaluable.

As AI evolves, the global community will increasingly need countries that can translate principles into practice at a population scale. The India AI Impact Summit is a chance to advance that work. If successful, its influence will extend far beyond India, shaping how the world understands and pursues responsible AI in the years ahead.

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