Alan Chan

Peter Wills

Markus Anderljung

Ben Garfinkel

Lennart Heim

Andrew Trask

Gabriel Mukobi

Rylan Schaeffer

Mauricio Baker

Sara Hooker

Irene Solaiman

Sasha Luccioni … (see 14 more)

Alexandra Luccioni

Nitarshan Rajkumar

Nicolas Moës

Jeffrey Ladish

David Bau

Paul Bricman

Neel Guha

Jessica Newman

Yoshua Bengio

Tobin South

Alex Pentland

Sanmi Koyejo

Mykel Kochenderfer

Robert Trager

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… (see more) 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.

2025-04-14

TMLR (accepted)

openreview.net

IDs for AI Systems

Noam Kolt

Peter Wills

Usman Anwar

Christian Schroeder de Witt

Nitarshan Rajkumar

Lewis Hammond

Lennart Heim

Markus Anderljung

2024-06-17

ArXiv (preprint)

IDs for AI Systems

Noam Kolt

Peter Wills

Usman Anwar

Christian Schroeder de Witt

Nitarshan Rajkumar

Lewis Hammond

Lennart Heim

Markus Anderljung

2024-06-17

ArXiv (preprint)

IDs for AI Systems

Noam Kolt

Peter Wills

Usman Anwar

Christian Schroeder de Witt

Nitarshan Rajkumar

Lewis Hammond

Lennart Heim

Markus Anderljung

2024-06-17

ArXiv (preprint)

IDs for AI Systems

Noam Kolt

Peter Wills

Usman Anwar

Christian Schroeder de Witt

Nitarshan Rajkumar

Lewis Hammond

Lennart Heim

Markus Anderljung

AI systems are increasingly pervasive, yet information needed to decide whether and how to engage with them may not exist or be accessible. … (see more)A user may not be able to verify whether a system has certain safety certifications. An investigator may not know whom to investigate when a system causes an incident. It may not be clear whom to contact to shut down a malfunctioning system. Across a number of domains, IDs address analogous problems by identifying particular entities (e.g., a particular Boeing 747) and providing information about other entities of the same class (e.g., some or all Boeing 747s). We propose a framework in which IDs are ascribed to instances of AI systems (e.g., a particular chat session with Claude 3), and associated information is accessible to parties seeking to interact with that system. We characterize IDs for AI systems, provide concrete examples where IDs could be useful, argue that there could be significant demand for IDs from key actors, analyze how those actors could incentivize ID adoption, explore a potential implementation of our framework for deployers of AI systems, and highlight limitations and risks. IDs seem most warranted in settings where AI systems could have a large impact upon the world, such as in making financial transactions or contacting real humans. With further study, IDs could help to manage a world where AI systems pervade society.

2024-06-17

ArXiv (preprint)

IDs for AI Systems

Noam Kolt

Peter Wills

Usman Anwar

Christian Schroeder de Witt

Nitarshan Rajkumar

Lewis Hammond

Lennart Heim

Markus Anderljung

AI systems are increasingly pervasive, yet information needed to decide whether and how to engage with them may not exist or be accessible. … (see more)A user may not be able to verify whether a system has certain safety certifications. An investigator may not know whom to investigate when a system causes an incident. It may not be clear whom to contact to shut down a malfunctioning system. Across a number of domains, IDs address analogous problems by identifying particular entities (e.g., a particular Boeing 747) and providing information about other entities of the same class (e.g., some or all Boeing 747s). We propose a framework in which IDs are ascribed to instances of AI systems (e.g., a particular chat session with Claude 3), and associated information is accessible to parties seeking to interact with that system. We characterize IDs for AI systems, provide concrete examples where IDs could be useful, argue that there could be significant demand for IDs from key actors, analyze how those actors could incentivize ID adoption, explore a potential implementation of our framework for deployers of AI systems, and highlight limitations and risks. IDs seem most warranted in settings where AI systems could have a large impact upon the world, such as in making financial transactions or contacting real humans. With further study, IDs could help to manage a world where AI systems pervade society.

2024-06-17

ArXiv (preprint)

IDs for AI Systems

Noam Kolt

Peter Wills

Usman Anwar

Christian Schroeder de Witt

Nitarshan Rajkumar

Lewis Hammond

Lennart Heim

Markus Anderljung

2024-06-17

ArXiv (preprint)

IDs for AI Systems

Noam Kolt

Peter Wills

Usman Anwar

Christian Schroeder de Witt

Nitarshan Rajkumar

Lewis Hammond

Lennart Heim

Markus Anderljung

AI systems are increasingly pervasive, yet information needed to decide whether and how to engage with them may not exist or be accessible. … (see more)A user may not be able to verify whether a system has certain safety certifications. An investigator may not know whom to investigate when a system causes an incident. It may not be clear whom to contact to shut down a malfunctioning system. Across a number of domains, IDs address analogous problems by identifying particular entities (e.g., a particular Boeing 747) and providing information about other entities of the same class (e.g., some or all Boeing 747s). We propose a framework in which IDs are ascribed to instances of AI systems (e.g., a particular chat session with Claude 3), and associated information is accessible to parties seeking to interact with that system. We characterize IDs for AI systems, provide concrete examples where IDs could be useful, argue that there could be significant demand for IDs from key actors, analyze how those actors could incentivize ID adoption, explore a potential implementation of our framework for deployers of AI systems, and highlight limitations and risks. IDs seem most warranted in settings where AI systems could have a large impact upon the world, such as in making financial transactions or contacting real humans. With further study, IDs could help to manage a world where AI systems pervade society.

2024-06-17

ArXiv (preprint)

IDs for AI Systems

Noam Kolt

Peter Wills

Usman Anwar

Christian Schroeder de Witt

Nitarshan Rajkumar

Lewis Hammond

Lennart Heim

Markus Anderljung

2024-06-17

ArXiv (preprint)

Black-Box Access is Insufficient for Rigorous AI Audits

Stephen Casper

Carson Ezell

Charlotte Siegmann

Noam Kolt

Taylor Lynn Curtis

Benjamin Bucknall

Andreas Haupt

Kevin Wei

Jérémy Scheurer

Marius Hobbhahn

Lee Sharkey

Satyapriya Krishna

Marvin Von Hagen

Silas Alberti

Qinyi Sun

Michael Gerovitch

David Bau

Max Tegmark

David Scott Krueger … (see 1 more)

Dylan Hadfield-Menell

External audits of AI systems are increasingly recognized as a key mechanism for AI governance. The effectiveness of an audit, however, depe… (see more)nds on the degree of system access granted to auditors. Recent audits of state-of-the-art AI systems have primarily relied on black-box access, in which auditors can only query the system and observe its outputs. However, white-box access to the system's inner workings (e.g., weights, activations, gradients) allows an auditor to perform stronger attacks, more thoroughly interpret models, and conduct fine-tuning. Meanwhile, outside-the-box access to its training and deployment information (e.g., methodology, code, documentation, hyperparameters, data, deployment details, findings from internal evaluations) allows for auditors to scrutinize the development process and design more targeted evaluations. In this paper, we examine the limitations of black-box audits and the advantages of white- and outside-the-box audits. We also discuss technical, physical, and legal safeguards for performing these audits with minimal security risks. Given that different forms of access can lead to very different levels of evaluation, we conclude that (1) transparency regarding the access and methods used by auditors is necessary to properly interpret audit results, and (2) white- and outside-the-box access allow for substantially more scrutiny than black-box access alone.

2024-06-05

The 2024 ACM Conference on Fairness, Accountability, and Transparency (published)

Visibility into AI Agents

Carson Ezell

Max Kaufmann

Kevin Wei

Lewis Hammond

Herbie Bradley

Emma Bluemke

Nitarshan Rajkumar

Noam Kolt

Lennart Heim

Markus Anderljung

2024-06-05

The 2024 ACM Conference on Fairness, Accountability, and Transparency (published)

Foundational Challenges in Assuring Alignment and Safety of Large Language Models

Usman Anwar

Abulhair Saparov

Javier Rando

Daniel Paleka

Miles Turpin

Peter Hase

Ekdeep Singh Lubana

Erik Jenner

Stephen Casper

Oliver Sourbut

Benjamin L. Edelman

Zhaowei Zhang

Mario Günther

Anton Korinek

Jose Hernandez-Orallo

Lewis Hammond

Eric J Bigelow

Alexander Pan

Lauro Langosco

Tomasz Korbak … (see 18 more)

Heidi Zhang

Ruiqi Zhong

Sean 'o H'eigeartaigh

Gabriel Recchia

Giulio Corsi

Markus Anderljung

Lilian Edwards

Yoshua Bengio

Danqi Chen

Samuel Albanie

Tegan Maharaj

Jakob Nicolaus Foerster

Florian Tramèr

He He

Atoosa Kasirzadeh

Yejin Choi

This work identifies 18 foundational challenges in assuring the alignment and safety of large language models (LLMs). These challenges are o… (see more)rganized into three different categories: scientific understanding of LLMs, development and deployment methods, and sociotechnical challenges. Based on the identified challenges, we pose

2024-04-15

ArXiv (preprint)