David Scott Krueger

Yann LeCun

Stephane Deny

2024-01-30

TMLR (accepté)

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

Alan Chan

Qinyi Sun

Michael Gerovitch

David Bau

Max Tegmark

David Scott Krueger … (voir 1 de plus)

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… (voir plus)nds on the degree of 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 training and deployment information (e.g., methodology, code, documentation, data, deployment details, findings from internal evaluations) allows 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.

2024-01-25

ArXiv (prépublication)

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

Alan Chan

Qinyi Sun

Michael Gerovitch

David Bau

Max Tegmark

David Scott Krueger … (voir 1 de plus)

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… (voir plus)nds on the degree of 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 training and deployment information (e.g., methodology, code, documentation, data, deployment details, findings from internal evaluations) allows 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.

2024-01-25

ArXiv (prépublication)

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

Alan Chan

Qinyi Sun

Michael Gerovitch

David Bau

Max Tegmark

David Scott Krueger … (voir 1 de plus)

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… (voir plus)nds on the degree of 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 training and deployment information (e.g., methodology, code, documentation, data, deployment details, findings from internal evaluations) allows 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.

2024-01-25

ArXiv (prépublication)

Visibility into AI Agents

Alan Chan

Carson Ezell

Max Kaufmann

Kevin Wei

Lewis Hammond

Herbie Bradley

Emma Bluemke

Nitarshan Rajkumar

Noam Kolt

Lennart Heim

Markus Anderljung

Increased delegation of commercial, scientific, governmental, and personal activities to AI agents—systems capable of pursuing complex goa… (voir plus)ls with limited supervision—may exacerbate existing societal risks and introduce new risks. Understanding and mitigating these risks involves critically evaluating existing governance structures, revising and adapting these structures where needed, and ensuring accountability of key stakeholders. Information about where, why, how, and by whom certain AI agents are used, which we refer to as visibility, is critical to these objectives. In this paper, we assess three categories of measures to increase visibility into AI agents: agent identifiers, real-time monitoring, and activity logging. For each, we outline potential implementations that vary in intrusiveness and informativeness. We analyze how the measures apply across a spectrum of centralized through decentralized deployment contexts, accounting for various actors in the supply chain including hardware and software service providers. Finally, we discuss the implications of our measures for privacy and concentration of power. Further work into understanding the measures and mitigating their negative impacts can help to build a foundation for the governance of AI agents.

2024-01-23

ArXiv (prépublication)

Visibility into AI Agents

Alan Chan

Carson Ezell

Max Kaufmann

Kevin Wei

Lewis Hammond

Herbie Bradley

Emma Bluemke

Nitarshan Rajkumar

Noam Kolt

Lennart Heim

Markus Anderljung

Increased delegation of commercial, scientific, governmental, and personal activities to AI agents—systems capable of pursuing complex goa… (voir plus)ls with limited supervision—may exacerbate existing societal risks and introduce new risks. Understanding and mitigating these risks involves critically evaluating existing governance structures, revising and adapting these structures where needed, and ensuring accountability of key stakeholders. Information about where, why, how, and by whom certain AI agents are used, which we refer to as visibility, is critical to these objectives. In this paper, we assess three categories of measures to increase visibility into AI agents: agent identifiers, real-time monitoring, and activity logging. For each, we outline potential implementations that vary in intrusiveness and informativeness. We analyze how the measures apply across a spectrum of centralized through decentralized deployment contexts, accounting for various actors in the supply chain including hardware and software service providers. Finally, we discuss the implications of our measures for privacy and concentration of power. Further work into understanding the measures and mitigating their negative impacts can help to build a foundation for the governance of AI agents.

2024-01-23

ArXiv (prépublication)

Visibility into AI Agents

Alan Chan

Carson Ezell

Max Kaufmann

Kevin Wei

Lewis Hammond

Herbie Bradley

Emma Bluemke

Nitarshan Rajkumar

Noam Kolt

Lennart Heim

Markus Anderljung

Increased delegation of commercial, scientific, governmental, and personal activities to AI agents—systems capable of pursuing complex goa… (voir plus)ls with limited supervision—may exacerbate existing societal risks and introduce new risks. Understanding and mitigating these risks involves critically evaluating existing governance structures, revising and adapting these structures where needed, and ensuring accountability of key stakeholders. Information about where, why, how, and by whom certain AI agents are used, which we refer to as visibility, is critical to these objectives. In this paper, we assess three categories of measures to increase visibility into AI agents: agent identifiers, real-time monitoring, and activity logging. For each, we outline potential implementations that vary in intrusiveness and informativeness. We analyze how the measures apply across a spectrum of centralized through decentralized deployment contexts, accounting for various actors in the supply chain including hardware and software service providers. Finally, we discuss the implications of our measures for privacy and concentration of power. Further work into understanding the measures and mitigating their negative impacts can help to build a foundation for the governance of AI agents.

2024-01-23

ArXiv (prépublication)

Mechanistically analyzing the effects of fine-tuning on procedurally defined tasks

Samyak Jain

Robert Kirk

Ekdeep Singh Lubana

Robert P. Dick

Hidenori Tanaka

Tim Rocktäschel

Edward Grefenstette

Fine-tuning large pre-trained models has become the de facto strategy for developing both task-specific and general-purpose machine learning… (voir plus) systems, including developing models that are safe to deploy. Despite its clear importance, there has been minimal work that explains how fine-tuning alters the underlying capabilities learned by a model during pretraining: does fine-tuning yield entirely novel capabilities or does it just modulate existing ones? We address this question empirically in synthetic, controlled settings where we can use mechanistic interpretability tools (e.g., network pruning and probing) to understand how the model's underlying capabilities are changing. We perform an extensive analysis of the effects of fine-tuning in these settings, and show that: (i) fine-tuning rarely alters the underlying model capabilities; (ii) a minimal transformation, which we call a `wrapper', is typically learned on top of the underlying model capabilities, creating the illusion that they have been modified; and (iii) further fine-tuning on a task where such ``wrapped capabilities'' are relevant leads to sample-efficient revival of the capability, i.e., the model begins reusing these capabilities after only a few gradient steps. This indicates that practitioners can unintentionally remove a model's safety wrapper merely by fine-tuning it on a, e.g., superficially unrelated, downstream task. We additionally perform analysis on language models trained on the TinyStories dataset to support our claims in a more realistic setup.

2024-01-16

ICLR.cc/2024/Conference (poster)

Reward Model Ensembles Help Mitigate Overoptimization

Thomas Coste

Usman Anwar

Robert Kirk

Reinforcement learning from human feedback (RLHF) is a standard approach for fine-tuning large language models to follow instructions. As pa… (voir plus)rt of this process, learned reward models are used to approximately model human preferences. However, as imperfect representations of the “true” reward, these learned reward models are susceptible to overoptimization. Gao et al. (2023) studied this phenomenon in a synthetic human feedback setup with a significantly larger “gold” reward model acting as the true reward (instead of humans) and showed that overoptimization remains a persistent problem regardless of the size of the proxy reward model and training data used. Using a similar setup, we conduct a systematic study to evaluate the efficacy of using ensemble-based conservative optimization objectives, specifically worst-case optimization (WCO) and uncertainty-weighted optimization (UWO), for mitigating reward model overoptimization when using two optimization methods: (a) best-of-n sampling (BoN) (b) proximal policy optimization (PPO). We additionally extend the setup of Gao et al. (2023) to include 25% label noise to better mirror real-world conditions. Both with and without label noise we find that conservative optimization practically eliminates overoptimization and improves performance by up to 70% for BoN sampling. For PPO, ensemble-based conservative optimization always reduces overoptimization and outperforms single reward model optimization. Moreover, combining it with a small KL penalty successfully prevents overoptimization at no performance cost. Overall, our results demonstrate that ensemble-based conservative optimization can effectively counter overoptimization.

2024-01-16

ICLR.cc/2024/Conference (poster)

Affirmative Safety: An Approach to Risk Management for Advanced Ai

Akash Wasil

Joshua Clymer

Emily Dardaman

Simeon Campos

Evan Murphy

2024-01-01

SSRN Electronic Journal (publié)

Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Stephen Casper

Xander Davies

Claudia Shi

Thomas Krendl Gilbert

Jérémy Scheurer

Javier Rando

Rachel Freedman

Tomasz Korbak

David Lindner

Pedro Freire

Tony Tong Wang

Samuel Marks

Charbel-Raphael Segerie

Micah Carroll

Andi Peng

Phillip Christoffersen

Mehul Damani

Stewart Slocum

Usman Anwar

Anand Siththaranjan … (voir 12 de plus)

Max Nadeau

Eric J Michaud

Jacob Pfau

Dmitrii Krasheninnikov

Xin Chen

Lauro Langosco

Peter Hase

Erdem Biyik

Anca Dragan

Dorsa Sadigh

Dylan Hadfield-Menell

2023-12-30

TMLR (accepté)

Hazards from Increasingly Accessible Fine-Tuning of Downloadable Foundation Models

Alan Chan

Benjamin Bucknall

Herbie Bradley

Public release of the weights of pretrained foundation models, otherwise known as downloadable access \citep{solaiman_gradient_2023}, enable… (voir plus)s fine-tuning without the prohibitive expense of pretraining. Our work argues that increasingly accessible fine-tuning of downloadable models may increase hazards. First, we highlight research to improve the accessibility of fine-tuning. We split our discussion into research that A) reduces the computational cost of fine-tuning and B) improves the ability to share that cost across more actors. Second, we argue that increasingly accessible fine-tuning methods may increase hazard through facilitating malicious use and making oversight of models with potentially dangerous capabilities more difficult. Third, we discuss potential mitigatory measures, as well as benefits of more accessible fine-tuning. Given substantial remaining uncertainty about hazards, we conclude by emphasizing the urgent need for the development of mitigations.

2023-12-22

ArXiv (prépublication)