arXiv subjects

Ben Garfinkel

Publications and source records attributed to Ben Garfinkel.

13 recordsLinked to original sources

arXiv2025

The Future of the AI Summit Series

This policy memo examines the evolution of the international AI Summit series, initiated at Bletchley Park in 2023 and continued through Seoul in 2024 and Paris in 2025, as a forum for cooperation on the governance of advanced artificial intelligence. It analyzes the factors underpinning the series' early successes and assesses challenges related to scope, participation, continuity, and institutional design. Drawing on comparisons with existing international governance models, the memo evaluates options for hosting arrangements, secretariat formats, participant selection, agenda setting, and meeting frequency. It proposes a set of design recommendations aimed at preserving the series' focus on advanced AI governance while balancing inclusivity, effectiveness, and long-term sustainability.

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arXiv2025

Third-party compliance reviews for frontier AI safety frameworks

Safety frameworks have emerged as a best practice for managing risks from frontier artificial intelligence (AI) systems. However, it may be difficult for stakeholders to know if companies are adhering to their frameworks. This paper explores a potential solution: third-party compliance reviews. During a third-party compliance review, an independent external party assesses whether a frontier AI company is complying with its safety framework. First, we discuss the main benefits and challenges of such reviews. On the one hand, they can increase compliance with safety frameworks and provide assurance to internal and external stakeholders. On the other hand, they can create information security risks, impose additional cost burdens, and cause reputational damage, but these challenges can be partially mitigated by drawing on best practices from other industries. Next, we answer practical questions about third-party compliance reviews, namely: (1) Who could conduct the review? (2) What information sources could the reviewer consider? (3) How could compliance with the safety framework be assessed? (4) What information about the review could be disclosed externally? (5) How could the findings guide development and deployment actions? (6) When could the reviews be conducted? For each question, we evaluate a set of plausible options. Finally, we suggest "minimalist", "more ambitious", and "comprehensive" approaches for each question that a frontier AI company could adopt.

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arXiv2025

International AI Safety Report

The first International AI Safety Report comprehensively synthesizes the current evidence on the capabilities, risks, and safety of advanced AI systems. The report was mandated by the nations attending the AI Safety Summit in Bletchley, UK. Thirty nations, the UN, the OECD, and the EU each nominated a representative to the report's Expert Advisory Panel. A total of 100 AI experts contributed, representing diverse perspectives and disciplines. Led by the report's Chair, these independent experts collectively had full discretion over the report's content.

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arXiv2024

Open Problems in Technical AI Governance

AI progress is creating a growing range of risks and opportunities, but it is often unclear how they should be navigated. In many cases, the barriers and uncertainties faced are at least partly technical. Technical AI governance, referring to technical analysis and tools for supporting the effective governance of AI, seeks to address such challenges. It can help to (a) identify areas where intervention is needed, (b) identify and assess the efficacy of potential governance actions, and (c) enhance governance options by designing mechanisms for enforcement, incentivization, or compliance. In this paper, we explain what technical AI governance is, why it is important, and present a taxonomy and incomplete catalog of its open problems. This paper is intended as a resource for technical researchers or research funders looking to contribute to AI governance.

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arXiv2024

From Principles to Rules: A Regulatory Approach for Frontier AI

Several jurisdictions are starting to regulate frontier artificial intelligence (AI) systems, i.e. general-purpose AI systems that match or exceed the capabilities present in the most advanced systems. To reduce risks from these systems, regulators may require frontier AI developers to adopt safety measures. The requirements could be formulated as high-level principles (e.g. 'AI systems should be safe and secure') or specific rules (e.g. 'AI systems must be evaluated for dangerous model capabilities following the protocol set forth in...'). These regulatory approaches, known as 'principle-based' and 'rule-based' regulation, have complementary strengths and weaknesses. While specific rules provide more certainty and are easier to enforce, they can quickly become outdated and lead to box-ticking. Conversely, while high-level principles provide less certainty and are more costly to enforce, they are more adaptable and more appropriate in situations where the regulator is unsure exactly what behavior would best advance a given regulatory objective. However, rule-based and principle-based regulation are not binary options. Policymakers must choose a point on the spectrum between them, recognizing that the right level of specificity may vary between requirements and change over time. We recommend that policymakers should initially (1) mandate adherence to high-level principles for safe frontier AI development and deployment, (2) ensure that regulators closely oversee how developers comply with these principles, and (3) urgently build up regulatory capacity. Over time, the approach should likely become more rule-based. Our recommendations are based on a number of assumptions, including (A) risks from frontier AI systems are poorly understood and rapidly evolving, (B) many safety practices are still nascent, and (C) frontier AI developers are best placed to innovate on safety practices.

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arXiv2023

Open-Sourcing Highly Capable Foundation Models: An evaluation of risks, benefits, and alternative methods for pursuing open-source objectives

Recent decisions by leading AI labs to either open-source their models or to restrict access to their models has sparked debate about whether, and how, increasingly capable AI models should be shared. Open-sourcing in AI typically refers to making model architecture and weights freely and publicly accessible for anyone to modify, study, build on, and use. This offers advantages such as enabling external oversight, accelerating progress, and decentralizing control over AI development and use. However, it also presents a growing potential for misuse and unintended consequences. This paper offers an examination of the risks and benefits of open-sourcing highly capable foundation models. While open-sourcing has historically provided substantial net benefits for most software and AI development processes, we argue that for some highly capable foundation models likely to be developed in the near future, open-sourcing may pose sufficiently extreme risks to outweigh the benefits. In such a case, highly capable foundation models should not be open-sourced, at least not initially. Alternative strategies, including non-open-source model sharing options, are explored. The paper concludes with recommendations for developers, standard-setting bodies, and governments for establishing safe and responsible model sharing practices and preserving open-source benefits where safe.

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arXiv2023

Model evaluation for extreme risks

Current approaches to building general-purpose AI systems tend to produce systems with both beneficial and harmful capabilities. Further progress in AI development could lead to capabilities that pose extreme risks, such as offensive cyber capabilities or strong manipulation skills. We explain why model evaluation is critical for addressing extreme risks. Developers must be able to identify dangerous capabilities (through "dangerous capability evaluations") and the propensity of models to apply their capabilities for harm (through "alignment evaluations"). These evaluations will become critical for keeping policymakers and other stakeholders informed, and for making responsible decisions about model training, deployment, and security.

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arXiv2023

Democratising AI: Multiple Meanings, Goals, and Methods

Numerous parties are calling for the democratisation of AI, but the phrase is used to refer to a variety of goals, the pursuit of which sometimes conflict. This paper identifies four kinds of AI democratisation that are commonly discussed: (1) the democratisation of AI use, (2) the democratisation of AI development, (3) the democratisation of AI profits, and (4) the democratisation of AI governance. Numerous goals and methods of achieving each form of democratisation are discussed. The main takeaway from this paper is that AI democratisation is a multifarious and sometimes conflicting concept that should not be conflated with improving AI accessibility. If we want to move beyond ambiguous commitments to democratising AI, to productive discussions of concrete policies and trade-offs, then we need to recognise the principal role of the democratisation of AI governance in navigating tradeoffs and risks across decisions around use, development, and profits.

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arXiv2023

Towards best practices in AGI safety and governance: A survey of expert opinion

A number of leading AI companies, including OpenAI, Google DeepMind, and Anthropic, have the stated goal of building artificial general intelligence (AGI) - AI systems that achieve or exceed human performance across a wide range of cognitive tasks. In pursuing this goal, they may develop and deploy AI systems that pose particularly significant risks. While they have already taken some measures to mitigate these risks, best practices have not yet emerged. To support the identification of best practices, we sent a survey to 92 leading experts from AGI labs, academia, and civil society and received 51 responses. Participants were asked how much they agreed with 50 statements about what AGI labs should do. Our main finding is that participants, on average, agreed with all of them. Many statements received extremely high levels of agreement. For example, 98% of respondents somewhat or strongly agreed that AGI labs should conduct pre-deployment risk assessments, dangerous capabilities evaluations, third-party model audits, safety restrictions on model usage, and red teaming. Ultimately, our list of statements may serve as a helpful foundation for efforts to develop best practices, standards, and regulations for AGI labs.

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arXiv2023

Exploring the Relevance of Data Privacy-Enhancing Technologies for AI Governance Use Cases

The development of privacy-enhancing technologies has made immense progress in reducing trade-offs between privacy and performance in data exchange and analysis. Similar tools for structured transparency could be useful for AI governance by offering capabilities such as external scrutiny, auditing, and source verification. It is useful to view these different AI governance objectives as a system of information flows in order to avoid partial solutions and significant gaps in governance, as there may be significant overlap in the software stacks needed for the AI governance use cases mentioned in this text. When viewing the system as a whole, the importance of interoperability between these different AI governance solutions becomes clear. Therefore, it is imminently important to look at these problems in AI governance as a system, before these standards, auditing procedures, software, and norms settle into place.

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arXiv2020

The Windfall Clause: Distributing the Benefits of AI for the Common Good

As the transformative potential of AI has become increasingly salient as a matter of public and political interest, there has been growing discussion about the need to ensure that AI broadly benefits humanity. This in turn has spurred debate on the social responsibilities of large technology companies to serve the interests of society at large. In response, ethical principles and codes of conduct have been proposed to meet the escalating demand for this responsibility to be taken seriously. As yet, however, few institutional innovations have been suggested to translate this responsibility into legal commitments which apply to companies positioned to reap large financial gains from the development and use of AI. This paper offers one potentially attractive tool for addressing such issues: the Windfall Clause, which is an ex ante commitment by AI firms to donate a significant amount of any eventual extremely large profits. By this we mean an early commitment that profits that a firm could not earn without achieving fundamental, economically transformative breakthroughs in AI capabilities will be donated to benefit humanity broadly, with particular attention towards mitigating any downsides from deployment of windfall-generating AI.

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arXiv2018

The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation

This report surveys the landscape of potential security threats from malicious uses of AI, and proposes ways to better forecast, prevent, and mitigate these threats. After analyzing the ways in which AI may influence the threat landscape in the digital, physical, and political domains, we make four high-level recommendations for AI researchers and other stakeholders. We also suggest several promising areas for further research that could expand the portfolio of defenses, or make attacks less effective or harder to execute. Finally, we discuss, but do not conclusively resolve, the long-term equilibrium of attackers and defenders.

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arXiv2017

On the Impossibility of Supersized Machines

In recent years, a number of prominent computer scientists, along with academics in fields such as philosophy and physics, have lent credence to the notion that machines may one day become as large as humans. Many have further argued that machines could even come to exceed human size by a significant margin. However, there are at least seven distinct arguments that preclude this outcome. We show that it is not only implausible that machines will ever exceed human size, but in fact impossible.

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