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Sören Mindermann

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International AI Safety Report Second Key Update: Technical Safeguards and Risk Management
Yoshua Bengio
Stephen Clare
Carina Prunkl
Maksym Andriushchenko
BEN BUCKNALL
Philip Fox
Nestor Maslej
Conor McGlynn
Malcolm Murray
Shalaleh Rismani
Stephen Casper
Jessica Newman
Daniel Privitera
Sören Mindermann
Daron Acemoglu
Thomas G. Dietterich
Fredrik Heintz
Geoffrey Hinton
Nick Jennings
Susan Leavy … (voir 17 de plus)
Teresa Ludermir
Vidushi Marda
Helen Margetts
John McDermid
Jane Munga
Arvind Narayanan
Alondra Nelson
Clara Neppel
Sarvapali D. (Gopal) Ramchurn
Stuart Russell
Marietje Schaake
Bernhard Schölkopf
Alvaro Soto
Lee Tiedrich
Gael Varoquaux
Andrew Yao
Ya-Qin Zhang
This is the Second Key Update to the 2025 International AI Safety Report. The First Key Update (1) discussed developments in the capabilitie… (voir plus)s of general-purpose AI models and systems and associated risks. This Key Update covers how various actors, including researchers, companies, and governments, are approaching risk management and technical mitigations for AI. The past year has seen important developments in AI risk management, including better techniques for training safer models and monitoring their outputs. While this represents tangible progress, significant gaps remain. It is often uncertain how effective current measures are at preventing harms, and effectiveness varies across time and applications. There are many opportunities to further strengthen existing safeguard techniques and to develop new ones. This Key Update provides a concise overview of critical developments in risk management practices and technical risk mitigation since the publication of the 2025 AI Safety Report in January. It highlights where progress is being made and where gaps remain. Above all, it aims to support policymakers, researchers, and the public in navigating a rapidly changing environment, helping them to make informed and timely decisions about the governance of general-purpose AI. Professor Yoshua BengioUniversité de Montréal / LawZero /Mila – Quebec AI Institute & Chair
2025-12-06
SuperIntelligence - Robotics - Safety & Alignment (publié)
doi.org
International AI Safety Report: First Key Update, Capabilities and Risk Implications
Prof. Yoshua Bengio
Stephen Clare
Carina Prunkl
Maksym Andriushchenko
BEN BUCKNALL
Philip Fox
Tiancheng Hu
Cameron Jones
Sam Manning
Nestor Maslej
Vasilios Mavroudis
Conor McGlynn
Malcolm Murray
Shalaleh Rismani
Charlotte Stix
Lucia Velasco
Nicole Wheeler
Daniel Privitera
Sören Mindermann
Daron Acemoglu … (voir 36 de plus)
Thomas G. Dietterich
Fredrik Heintz
Geoffrey Hinton
Nick Jennings
Susan Leavy
Teresa Ludermir
Vidushi Marda
Helen Margetts
John McDermid
Jane Munga
Arvind Narayanan
Alondra Nelson
Clara Neppel
Sarvapali D. (Gopal) Ramchurn
Stuart Russell
Marietje Schaake
Bernhard Schölkopf
Alvaro Soto
Lee Tiedrich
Gael Varoquaux
Andrew Yao
Ya-Qin Zhang
Lambrini Das
Claire Dennis
Arianna Dini
Freya Hempleman
Samuel Kenny
Patrick King
Hannah Merchant
Jamie-Day Rawal
Rose Woolhouse
Jonathan Barry
Marc-Antoine Guérard
Claire Latendresse
Cassidy MacNeil
Benjamin Prud'homme
The field of AI is moving too quickly for a single yearly publication to keep pace. Significant changes can occur on a timescale of months, … (voir plus)sometimes weeks. This is why we are releasing Key Updates: shorter, focused reports that highlight the most important developments between full editions of the International AI Safety Report. With these updates, we aim to provide policymakers, researchers, and the public with up-to-date information to support wise decisions about AI governance. This first Key Update focuses on areas where especially significant changes have occurred since January 2025: advances in general-purpose AI systems' capabilities, and the implications for several critical risks. New training techniques have enabled AI systems to reason step-by-step and operate autonomously for longer periods, allowing them to tackle more kinds of work. However, these same advances create new challenges across biological risks, cyber security, and oversight of AI systems themselves. The International AI Safety Report is intended to help readers assess, anticipate, and manage risks from general-purpose AI systems. These Key Updates ensure that critical developments receive timely attention as the field rapidly evolves.
2025-10-21
SuperIntelligence - Robotics - Safety & Alignment (publié)
doi.org
Superintelligent Agents Pose Catastrophic Risks: Can Scientist AI Offer a Safer Path?
Yoshua Bengio
Michael Cohen
Damiano Fornasiere
Joumana Ghosn
Pietro Greiner
Matt MacDermott
Sören Mindermann
Adam Oberman
Jesse Richardson
Oliver Richardson
Marc-Antoine Rondeau
Pierre-Luc St-Charles
David Williams-King
The leading AI companies are increasingly focused on building generalist AI agents -- systems that can autonomously plan, act, and pursue go… (voir plus)als across almost all tasks that humans can perform. Despite how useful these systems might be, unchecked AI agency poses significant risks to public safety and security, ranging from misuse by malicious actors to a potentially irreversible loss of human control. We discuss how these risks arise from current AI training methods. Indeed, various scenarios and experiments have demonstrated the possibility of AI agents engaging in deception or pursuing goals that were not specified by human operators and that conflict with human interests, such as self-preservation. Following the precautionary principle, we see a strong need for safer, yet still useful, alternatives to the current agency-driven trajectory. Accordingly, we propose as a core building block for further advances the development of a non-agentic AI system that is trustworthy and safe by design, which we call Scientist AI. This system is designed to explain the world from observations, as opposed to taking actions in it to imitate or please humans. It comprises a world model that generates theories to explain data and a question-answering inference machine. Both components operate with an explicit notion of uncertainty to mitigate the risks of overconfident predictions. In light of these considerations, a Scientist AI could be used to assist human researchers in accelerating scientific progress, including in AI safety. In particular, our system can be employed as a guardrail against AI agents that might be created despite the risks involved. Ultimately, focusing on non-agentic AI may enable the benefits of AI innovation while avoiding the risks associated with the current trajectory. We hope these arguments will motivate researchers, developers, and policymakers to favor this safer path.
2025-08-14
SuperIntelligence - Robotics - Safety & Alignment (publié)
doi.org
arxiv.org
Open Problems in Machine Unlearning for AI Safety
Fazl Barez
Tingchen Fu
Ameya Prabhu
Stephen Casper
Amartya Sanyal
Adel Bibi
Aidan O'Gara
Robert Kirk
Benjamin Bucknall
Timothy Fist
Luke Ong
Philip Torr
Kwok-Yan Lam
Robert Trager
David M. Krueger
Sören Mindermann
Jose Hernandez-Orallo
Mor Geva
Yarin Gal
As AI systems become more capable, widely deployed, and increasingly autonomous in critical areas such as cybersecurity, biological research… (voir plus), and healthcare, ensuring their safety and alignment with human values is paramount. Machine unlearning -- the ability to selectively forget or suppress specific types of knowledge -- has shown promise for privacy and data removal tasks, which has been the primary focus of existing research. More recently, its potential application to AI safety has gained attention. In this paper, we identify key limitations that prevent unlearning from serving as a comprehensive solution for AI safety, particularly in managing dual-use knowledge in sensitive domains like cybersecurity and chemical, biological, radiological, and nuclear (CBRN) safety. In these contexts, information can be both beneficial and harmful, and models may combine seemingly harmless information for harmful purposes -- unlearning this information could strongly affect beneficial uses. We provide an overview of inherent constraints and open problems, including the broader side effects of unlearning dangerous knowledge, as well as previously unexplored tensions between unlearning and existing safety mechanisms. Finally, we investigate challenges related to evaluation, robustness, and the preservation of safety features during unlearning. By mapping these limitations and open challenges, we aim to guide future research toward realistic applications of unlearning within a broader AI safety framework, acknowledging its limitations and highlighting areas where alternative approaches may be required.
2025-01-08
ArXiv (prépublication)
doi.org
arxiv.org
International AI Safety Report
Yoshua Bengio
Bronwyn Fox
André Carlos Ponce de Leon Ferreira de Carvalho
Mona Nemer
Raquel Pezoa Rivera
Yi Zeng
Juha Heikkilä
Guillaume Avrin
Antonio Krüger
Balaraman Ravindran
Hammam Riza
Ciarán Seoighe
Ziv Katzir
Andrea Monti
Hiroaki Kitano
Nusu Mwamanzi
Fahad Albalawi
José Ramón López Portillo
Haroon Sheikh
Gill Jolly … (voir 86 de plus)
Olubunmi Ajala
Jerry Sheehan
Dominic Vincent Ligot
Kyoung Mu Lee
Crystal Rugege
Denise Wong
Nuria Oliver
Christian Busch
Ahmet Halit Hatip
Oleksii Molchanovskyi
Marwan Alserkal
Chris Johnson
Amandeep Singh Gill
Saif M. Khan
Sören Mindermann
Daniel Privitera
Tamay Besiroglu
Rishi Bommasani
Stephen Casper
Yejin Choi
Philip Fox
Ben Garfinkel
Danielle Goldfarb
Hoda Heidari
Anson Ho
Sayash Kapoor
Leila Khalatbari
Shayne Longpre
Sam Manning
Vasilios Mavroudis
Mantas Mazeika
Julian Michael
Jessica Newman
Kwan Yee Ng
Chinasa T. Okolo
Deborah Raji
Girish Sastry
Elizabeth Seger
Theodora Skeadas
Tobin South
Daron Acemoglu
Olubayo Adekanmbi
David Dalrymple
Thomas G. Dietterich
Edward W. Felten
Pascale Fung
Pierre-Olivier Gourinchas
Fredrik Heintz
Geoffrey Hinton
Nick Jennings
Andreas Krause
Susan Leavy
Percy Liang
Teresa Ludermir
Vidushi Marda
Emma Strubell
Florian Tramèr
Lucia Velasco
Nicole Wheeler
Helen Margetts
John McDermid
Jane Munga
Arvind Narayanan
Alondra Nelson
Clara Neppel
Alice Oh
Gopal Ramchurn
Stuart Russell
Marietje Schaake
Bernhard Schölkopf
Dawn Song
Alvaro Soto
Lee Tiedrich
Gael Varoquaux
Andrew Yao
Ya-Qin Zhang
Baran Acar
Ben Clifford
Lambrini Das
Claire Dennis
Freya Hempleman
Hannah Merchant
Rian Overy
Ben Snodin
Jonathan Barry
Benjamin Prud’homme
The first International AI Safety Report comprehensively synthesizes the current evidence on the capabilities, risks, and safety of advanced… (voir plus) 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.
2024-12-31
arXiv (prépublication)
doi.org
arxiv.org
Open Technical Problems in Open-Weight AI Model Risk Management
Stephen Casper
Kyle O'Brien
Shayne Longpre
Elizabeth Seger
Kevin Klyman
Rishi Bommasani
Aniruddha Nrusimha
Ilia Shumailov
Sören Mindermann
Steven Basart
Frank Rudzicz
Kellin Pelrine
Avijit Ghosh
Andrew Strait
Robert Kirk
Dan Hendrycks
Peter Henderson
J. Zico Kolter
Geoffrey Irving
Yarin Gal … (voir 2 de plus)
Yoshua Bengio
Dylan Hadfield-Menell
2024-12-31
SSRN Electronic Journal (accepté)
doi.org
Superintelligent Agents Pose Catastrophic Risks: Can Scientist AI Offer a Safer Path?
Yoshua Bengio
Michael Cohen
Damiano Fornasiere
Joumana Ghosn
Pietro Greiner
Matt MacDermott
Sören Mindermann
Adam Oberman
Jesse Richardson
Oliver Richardson
Marc-Antoine Rondeau
Pierre-Luc St-Charles
David Williams-King
The leading AI companies are increasingly focused on building generalist AI agents -- systems that can autonomously plan, act, and pursue go… (voir plus)als across almost all tasks that humans can perform. Despite how useful these systems might be, unchecked AI agency poses significant risks to public safety and security, ranging from misuse by malicious actors to a potentially irreversible loss of human control. We discuss how these risks arise from current AI training methods. Indeed, various scenarios and experiments have demonstrated the possibility of AI agents engaging in deception or pursuing goals that were not specified by human operators and that conflict with human interests, such as self-preservation. Following the precautionary principle, we see a strong need for safer, yet still useful, alternatives to the current agency-driven trajectory. Accordingly, we propose as a core building block for further advances the development of a non-agentic AI system that is trustworthy and safe by design, which we call Scientist AI. This system is designed to explain the world from observations, as opposed to taking actions in it to imitate or please humans. It comprises a world model that generates theories to explain data and a question-answering inference machine. Both components operate with an explicit notion of uncertainty to mitigate the risks of overconfident predictions. In light of these considerations, a Scientist AI could be used to assist human researchers in accelerating scientific progress, including in AI safety. In particular, our system can be employed as a guardrail against AI agents that might be created despite the risks involved. Ultimately, focusing on non-agentic AI may enable the benefits of AI innovation while avoiding the risks associated with the current trajectory. We hope these arguments will motivate researchers, developers, and policymakers to favor this safer path.
2024-12-31
arXiv (prépublication)
doi.org
arxiv.org
The Singapore Consensus on Global AI Safety Research Priorities
Yoshua Bengio
Tegan Maharaj
Luke Ong
Stuart Russell
Dawn Song
Max Tegmark
Lan Xue
Ya-Qin Zhang
Stephen Casper
Wan Sie Lee
Sören Mindermann
Vanessa Wilfred
Vidhisha Balachandran
Fazl Barez
Michael Belinsky
Ima Bello
Malo Bourgon
Mark Brakel
Simeon Campos
Duncan Cass-Beggs … (voir 67 de plus)
Jiahao Chen
Rumman Chowdhury
Chua Kuan Seah
Jeff Clune
Juntao Dai
Agnes Delaborde
Nouha Dziri
Francisco Eiras
Joshua Engels
Jinyu Fan
Adam Gleave
Noah Goodman
Fynn Heide
Johannes Heidecke
Dan Hendrycks
Cyrus Hodes
Bryan Low
Minlie Huang
Sami Jawhar
Jingyu Wang
Adam Kalai
Meindert Kamphuis
Mohan Kankanhalli
Subhash Kantamneni
Mathias Kirk Bonde
Thomas Kwa
Jeffrey Ladish
Kwok Yan Lam
Wan Sie Lee
Taewhi Lee
Xiaojian Li
Jiajun Liu
Chaochao Lu
Yifan Mai
Richard Mallah
Julian Michael
Nicolas Moës
Simon Moeller
Kihyuk Nam
Kwan Yee Ng
Mark Nitzberg
Besmira Nushi
Seán Ó hÉigeartaigh
Alejandro Ortega
Pierre Peigné
James Petrie
Benjamin Prud'homme
Reihaneh Rabbany
Nayat Sanchez-Pi
Sarah Schwettmann
Buck Shlegeris
SAAD SIDDIQUI
Anu Sinha
Martin Soto
Cheston Tan
Anthony Tung
William Tjhi
Robert Trager
Brian Tse
Anthony Tung
John Willes
Denise Wong
Wei Xu
Rongwu Xu
Yi Zeng
Hongjiang Zhang
Djordje Zikelic
Rapidly improving AI capabilities and autonomy hold significant promise of transformation, but are also driving vigorous debate on how to en… (voir plus)sure that AI is safe, i.e., trustworthy, reliable, and secure. Building a trusted ecosystem is therefore essential – it helps people embrace AI with confidence and gives maximal space for innovation while avoiding backlash. This requires policymakers, industry, researchers and the broader public to collectively work toward securing positive outcomes from AI’s development. AI safety research is a key dimension. Given that the state of science today for building trustworthy AI does not fully cover all risks, accelerated investment in research is required to keep pace with commercially driven growth in system capabilities. Goals: The 2025 Singapore Conference on AI (SCAI): International Scientific Exchange on AI Safety aims to support research in this important space by bringing together AI scientists across geographies to identify and synthesise research priorities in AI safety. The result, The Singapore Consensus on Global AI Safety Research Priorities, builds on the International AI Safety Report-A (IAISR) chaired by Yoshua Bengio and backed by 33 governments. By adopting a defence-in-depth model, this document organises AI safety research domains into three types: challenges with creating trustworthy AI systems (Development), challenges with evaluating their risks (Assessment), and challenges with monitoring and intervening after deployment (Control). Through the Singapore Consensus, we hope to globally facilitate meaningful conversations between AI scientists and AI policymakers for maximally beneficial outcomes. Our goal is to enable more impactful R&D efforts to rapidly develop safety and evaluation mechanisms and foster a trusted ecosystem where AI is harnessed for the public good.
2024-12-31
arXiv.org (prépublication)
doi.org
arxiv.org
In Which Areas of Technical AI Safety Could Geopolitical Rivals Cooperate?
BEN BUCKNALL
SAAD SIDDIQUI
LARA THURNHERR
CONOR MCGURK
BEN HARACK
Anka Reuel
PATRICIA PASKOV
CASEY MAHONEY
Sören Mindermann
Scott Singer
VINAY HIREMATH
Charbel-Raphael Segerie
OSCAR DELANEY
Alessandro Abate
Fazl Barez
Michael K. Cohen
Philip Torr
FERENC HUSZÁR
ANISOARA CALINESCU
GABRIEL DAVIS JONES … (voir 2 de plus)
Yoshua Bengio
Robert Trager
International cooperation is common in AI research, including between geopolitical rivals. While many experts advocate for greater internati… (voir plus)onal cooperation on AI safety to address shared global risks, some view cooperation on AI with suspicion, arguing that it can pose unacceptable risks to national security. However, the extent to which cooperation on AI safety poses such risks, as well as provides benefits, depends on the specific area of cooperation. In this paper, we consider technical factors that impact the risks of international cooperation on AI safety research, focusing on the degree to which such cooperation can advance dangerous capabilities, result in the sharing of sensitive information, or provide opportunities for harm. We begin by why nations historically cooperate on strategic technologies and analyse current US-China cooperation in AI as a case study. We further argue that existing frameworks for managing associated risks can be supplemented with consideration of key risks specific to cooperation on technical AI safety research. Through our analysis, we find that research into AI verification mechanisms and shared protocols may be suitable areas for such cooperation. Through this analysis we aim to help researchers and governments identify and mitigate the risks of international cooperation on AI safety research, so that the benefits of cooperation can be fully realised.
2024-12-31
ACM Conference on Fairness, Accountability, and Transparency (publié)
doi.org
arxiv.org
Managing extreme AI risks amid rapid progress
Yoshua Bengio
Geoffrey Hinton
Andrew Yao
Dawn Song
Pieter Abbeel
Yuval Noah Harari
Trevor Darrell
Ya-Qin Zhang
Lan Xue
Shai Shalev-Shwartz
Gillian Hadfield
Jeff Clune
Tegan Maharaj
Frank Hutter
Atilim Güneş Baydin
Sheila McIlraith
Qiqi Gao
Ashwin Acharya
David Krueger
Anca Dragan … (voir 5 de plus)
Philip Torr
Stuart Russell
Daniel Kahneman
Jan Brauner
Sören Mindermann
Artificial Intelligence (AI) is progressing rapidly, and companies are shifting their focus to developing generalist AI systems that can aut… (voir plus)onomously act and pursue goals. Increases in capabilities and autonomy may soon massively amplify AI's impact, with risks that include large-scale social harms, malicious uses, and an irreversible loss of human control over autonomous AI systems. Although researchers have warned of extreme risks from AI, there is a lack of consensus about how exactly such risks arise, and how to manage them. Society's response, despite promising first steps, is incommensurate with the possibility of rapid, transformative progress that is expected by many experts. AI safety research is lagging. Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness, and barely address autonomous systems. In this short consensus paper, we describe extreme risks from upcoming, advanced AI systems. Drawing on lessons learned from other safety-critical technologies, we then outline a comprehensive plan combining technical research and development with proactive, adaptive governance mechanisms for a more commensurate preparation.
2023-10-25
Science (publié)
doi.org
arxiv.org