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.

2025-01-09

ArXiv (prépublication)

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

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.

2025-01-09

ArXiv (prépublication)

arxiv.org

The Singapore Consensus on Global AI Safety Research Priorities

Luke Ong

Stuart Russell

Dawn Song

Max Tegmark

Lan Xue

Ya-Qin Zhang

Stephen Casper

Wan Sie Lee

Vanessa Wilfred

Vidhisha Balachandran

Fazl Barez

Michael Belinsky

Imane Bello

Malo Bourgon

Mark Brakel

Sim'eon Campos

Duncan Cass-Beggs … (voir 67 de plus)

Jiahao Chen

Rumman Chowdhury

Kuan Chua Seah

Jeff Clune

Juntao Dai

Agnès Delaborde

Nouha Dziri

Francisco Eiras

Joshua Engels

Jinyu Fan

Adam Gleave

Noah D. Goodman

Fynn Heide

Johannes Heidecke

Dan Hendrycks

Cyrus Hodes

Bryan Low Kian Hsiang

Minlie Huang

Sami Jawhar

Jingyu Wang

Adam Tauman Kalai

Meindert Kamphuis

Mohan S. Kankanhalli

Subhash Kantamneni

Mathias Bonde Kirk

Thomas Kwa

Jeffrey Ladish

Kwok-Yan Lam

Wan Lee Sie

Taewhi Lee

Xiaojian Li

Jiajun Liu

Chaochao Lu

Yifan Mai

Richard Mallah

Julian Michael

Nick Moës

Simon Möller

Kihyuk Nam

Kwan Yee Ng

Mark Nitzberg

Besmira Nushi

Sean O hEigeartaigh

Alejandro Ortega

Pierre Peigné

James Petrie

Benjamin Prud'homme

Reihaneh Rabbany

Nayat Sanchez-Pi

Sarah Schwettmann

Buck Shlegeris

Saad Siddiqui

Aradhana Sinha

Martín Soto

Cheston Tan

Dong Ting

William-Chandra Tjhi

Robert Trager

Brian Tse

H. AnthonyTungK.

John Willes

Denise Wong

W. 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. The "2025 Singapore Conference on AI (SCAI): International Scientific Exchange on AI Safety" aimed to support research in this space by bringing together AI scientists across geographies to identify and synthesise research priorities in AI safety. This resulting report builds on the International AI Safety Report chaired by Yoshua Bengio and backed by 33 governments. By adopting a defence-in-depth model, this report 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).

2025-01-01

arXiv.org (prépublication)

arxiv.org

The Singapore Consensus on Global AI Safety Research Priorities

Luke Ong

Stuart Russell

Dawn Song

Max Tegmark

Lan Xue

Ya-Qin Zhang

Stephen Casper

Wan Sie Lee

Vanessa Wilfred

Vidhisha Balachandran

Fazl Barez

Michael Belinsky

Imane Bello

Malo Bourgon

Mark Brakel

Sim'eon Campos

Duncan Cass-Beggs … (voir 67 de plus)

Jiahao Chen

Rumman Chowdhury

Kuan Chua Seah

Jeff Clune

Juntao Dai

Agnès Delaborde

Nouha Dziri

Francisco Eiras

Joshua Engels

Jinyu Fan

Adam Gleave

Noah D. Goodman

Fynn Heide

Johannes Heidecke

Dan Hendrycks

Cyrus Hodes

Bryan Low Kian Hsiang

Minlie Huang

Sami Jawhar

Jingyu Wang

Adam Tauman Kalai

Meindert Kamphuis

Mohan S. Kankanhalli

Subhash Kantamneni

Mathias Bonde Kirk

Thomas Kwa

Jeffrey Ladish

Kwok-Yan Lam

Wan Lee Sie

Taewhi Lee

Xiaojian Li

Jiajun Liu

Chaochao Lu

Yifan Mai

Richard Mallah

Julian Michael

Nick Moës

Simon Möller

Kihyuk Nam

Kwan Yee Ng

Mark Nitzberg

Besmira Nushi

Sean O hEigeartaigh

Alejandro Ortega

Pierre Peigné

James Petrie

Benjamin Prud'homme

Reihaneh Rabbany

Nayat Sanchez-Pi

Sarah Schwettmann

Buck Shlegeris

Saad Siddiqui

Aradhana Sinha

Martín Soto

Cheston Tan

Dong Ting

William Tjhi

Robert Trager

Brian Tse

H. AnthonyTungK.

John Willes

Denise Wong

Wei Xu

Rongwu Xu

Yi Zeng 0005

HongJiang Zhang

Djordje Zikelic

2025-01-01

arXiv.org (prépublication)

Managing extreme AI risks amid rapid progress

Geoffrey Hinton

Andrew Yao

Dawn Song

Pieter Abbeel

Trevor Darrell

Yuval Noah Harari

Ya-Qin Zhang

Lan Xue

Shai Shalev-Shwartz

Gillian K. Hadfield

Jeff Clune

Frank Hutter

Atilim Güneş Baydin

Sheila McIlraith

Qiqi Gao

Ashwin Acharya

Anca Dragan … (voir 5 de plus)

Philip Torr

Stuart Russell

Daniel Kahneman

Jan Brauner

Preparation requires technical research and development, as well as adaptive, proactive governance Artificial intelligence (AI) is progressi… (voir plus)ng rapidly, and companies are shifting their focus to developing generalist AI systems that can autonomously 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 (1), there is a lack of consensus about 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. Drawing on lessons learned from other safety-critical technologies, we outline a comprehensive plan that combines technical research and development (R&D) with proactive, adaptive governance mechanisms for a more commensurate preparation.

2023-10-26

Science (publié)

Managing extreme AI risks amid rapid progress

Geoffrey Hinton

Andrew Yao

Dawn Song

Pieter Abbeel

Trevor Darrell

Yuval Noah Harari

Ya-Qin Zhang

Lan Xue

Shai Shalev-Shwartz

Gillian K. Hadfield

Jeff Clune

Frank Hutter

Atilim Güneş Baydin

Sheila McIlraith

Qiqi Gao

Ashwin Acharya

Anca Dragan … (voir 5 de plus)

Philip Torr

Stuart Russell

Daniel Kahneman

Jan Brauner

2023-10-26

Science (publié)

Managing extreme AI risks amid rapid progress

Geoffrey Hinton

Andrew Yao

Dawn Song

Pieter Abbeel

Trevor Darrell

Yuval Noah Harari

Ya-Qin Zhang

Lan Xue

Shai Shalev-Shwartz

Gillian K. Hadfield

Jeff Clune

Frank Hutter

Atilim Güneş Baydin

Sheila McIlraith

Qiqi Gao

Ashwin Acharya

Anca Dragan … (voir 5 de plus)

Philip Torr

Stuart Russell

Daniel Kahneman

Jan Brauner

Preparation requires technical research and development, as well as adaptive, proactive governance Artificial intelligence (AI) is progressi… (voir plus)ng rapidly, and companies are shifting their focus to developing generalist AI systems that can autonomously 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 (1), there is a lack of consensus about 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. Drawing on lessons learned from other safety-critical technologies, we outline a comprehensive plan that combines technical research and development (R&D) with proactive, adaptive governance mechanisms for a more commensurate preparation.

2023-10-26

Science (publié)

Managing extreme AI risks amid rapid progress

Geoffrey Hinton

Andrew Yao

Dawn Song

Pieter Abbeel

Trevor Darrell

Yuval Noah Harari

Ya-Qin Zhang

Lan Xue

Shai Shalev-Shwartz

Gillian K. Hadfield

Jeff Clune

Frank Hutter

Atilim Güneş Baydin

Sheila McIlraith

Qiqi Gao

Ashwin Acharya

Anca Dragan … (voir 5 de plus)

Philip Torr

Stuart Russell

Daniel Kahneman

Jan Brauner

2023-10-26

Science (publié)

Managing extreme AI risks amid rapid progress

Geoffrey Hinton

Andrew Yao

Dawn Song

Pieter Abbeel

Trevor Darrell

Yuval Noah Harari

Ya-Qin Zhang

Lan Xue

Shai Shalev-Shwartz

Gillian K. Hadfield

Jeff Clune

Frank Hutter

Atilim Güneş Baydin

Sheila McIlraith

Qiqi Gao

Ashwin Acharya

Anca Dragan … (voir 5 de plus)

Philip Torr

Stuart Russell

Daniel Kahneman

Jan Brauner

Preparation requires technical research and development, as well as adaptive, proactive governance Artificial intelligence (AI) is progressi… (voir plus)ng rapidly, and companies are shifting their focus to developing generalist AI systems that can autonomously 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 (1), there is a lack of consensus about 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. Drawing on lessons learned from other safety-critical technologies, we outline a comprehensive plan that combines technical research and development (R&D) with proactive, adaptive governance mechanisms for a more commensurate preparation.

2023-10-26

Science (publié)

Managing extreme AI risks amid rapid progress

Geoffrey Hinton

Andrew Yao

Dawn Song

Pieter Abbeel

Trevor Darrell

Yuval Noah Harari

Ya-Qin Zhang

Lan Xue

Shai Shalev-Shwartz

Gillian K. Hadfield

Jeff Clune

Frank Hutter

Atilim Güneş Baydin

Sheila McIlraith

Qiqi Gao

Ashwin Acharya

Anca Dragan … (voir 5 de plus)

Philip Torr

Stuart Russell

Daniel Kahneman

Jan Brauner

Preparation requires technical research and development, as well as adaptive, proactive governance Artificial intelligence (AI) is progressi… (voir plus)ng rapidly, and companies are shifting their focus to developing generalist AI systems that can autonomously 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 (1), there is a lack of consensus about 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. Drawing on lessons learned from other safety-critical technologies, we outline a comprehensive plan that combines technical research and development (R&D) with proactive, adaptive governance mechanisms for a more commensurate preparation.

2023-10-26

Science (publié)

Managing extreme AI risks amid rapid progress

Geoffrey Hinton

Andrew Yao

Dawn Song

Pieter Abbeel

Trevor Darrell

Yuval Noah Harari

Ya-Qin Zhang

Lan Xue

Shai Shalev-Shwartz

Gillian K. Hadfield

Jeff Clune

Frank Hutter

Atilim Güneş Baydin

Sheila McIlraith

Qiqi Gao

Ashwin Acharya

Anca Dragan … (voir 5 de plus)

Philip Torr

Stuart Russell

Daniel Kahneman

Jan Brauner

Preparation requires technical research and development, as well as adaptive, proactive governance Artificial intelligence (AI) is progressi… (voir plus)ng rapidly, and companies are shifting their focus to developing generalist AI systems that can autonomously 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 (1), there is a lack of consensus about 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. Drawing on lessons learned from other safety-critical technologies, we outline a comprehensive plan that combines technical research and development (R&D) with proactive, adaptive governance mechanisms for a more commensurate preparation.

2023-10-26

Science (publié)

Managing extreme AI risks amid rapid progress

Geoffrey Hinton

Andrew Yao

Dawn Song

Pieter Abbeel

Trevor Darrell

Yuval Noah Harari

Ya-Qin Zhang

Lan Xue

Shai Shalev-Shwartz

Gillian K. Hadfield

Jeff Clune

Frank Hutter

Atilim Güneş Baydin

Sheila McIlraith

Qiqi Gao

Ashwin Acharya

Anca Dragan … (voir 5 de plus)

Philip Torr

Stuart Russell

Daniel Kahneman

Jan Brauner

Preparation requires technical research and development, as well as adaptive, proactive governance Artificial intelligence (AI) is progressi… (voir plus)ng rapidly, and companies are shifting their focus to developing generalist AI systems that can autonomously 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 (1), there is a lack of consensus about 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. Drawing on lessons learned from other safety-critical technologies, we outline a comprehensive plan that combines technical research and development (R&D) with proactive, adaptive governance mechanisms for a more commensurate preparation.

2023-10-26

Science (publié)