David Scott Krueger

Lennart Heim

Markus Anderljung

2024-06-17

ArXiv (prépublication)

IDs for AI Systems

Alan Chan

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. … (voir plus)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 (prépublication)

IDs for AI Systems

Alan Chan

Noam Kolt

Peter Wills

Usman Anwar

Christian Schroeder de Witt

Nitarshan Rajkumar

Lewis Hammond

Lennart Heim

Markus Anderljung

2024-06-17

ArXiv (prépublication)

IDs for AI Systems

Alan Chan

Noam Kolt

Peter Wills

Usman Anwar

Christian Schroeder de Witt

Nitarshan Rajkumar

Lewis Hammond

Lennart Heim

Markus Anderljung

2024-06-17

ArXiv (prépublication)

IDs for AI Systems

Alan Chan

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. … (voir plus)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 (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 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 (publié)

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

2024-06-05

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

Stress-Testing Capability Elicitation With Password-Locked Models

Ryan Greenblatt

Fabien Roger

Dmitrii Krasheninnikov

2024-05-29

ArXiv (prépublication)

Stress-Testing Capability Elicitation With Password-Locked Models

Ryan Greenblatt

Fabien Roger

Dmitrii Krasheninnikov

To determine the safety of large language models (LLMs), AI developers must be able to assess their dangerous capabilities. But simple promp… (voir plus)ting strategies often fail to elicit an LLM's full capabilities. One way to elicit capabilities more robustly is to fine-tune the LLM to complete the task. In this paper, we investigate the conditions under which fine-tuning-based elicitation suffices to elicit capabilities. To do this, we introduce password-locked models, LLMs fine-tuned such that some of their capabilities are deliberately hidden. Specifically, these LLMs are trained to exhibit these capabilities only when a password is present in the prompt, and to imitate a much weaker LLM otherwise. Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked capabilities can be elicited without using the password. We find that a few high-quality demonstrations are often sufficient to fully elicit password-locked capabilities. More surprisingly, fine-tuning can elicit other capabilities that have been locked using the same password, or even different passwords. Furthermore, when only evaluations, and not demonstrations, are available, approaches like reinforcement learning are still often able to elicit capabilities. Overall, our findings suggest that fine-tuning is an effective method of eliciting hidden capabilities of current models, but may be unreliable when high-quality demonstrations are not available, e.g. as may be the case when models' (hidden) capabilities exceed those of human demonstrators.

2024-05-29

ArXiv (prépublication)

Implicitly Bayesian Prediction Rules in Deep Learning

Bruno Mlodozeniec

Richard E. Turner

The Bayesian approach leads to coherent updates of predictions under new data, which makes adhering to Bayesian principles appealing in deci… (voir plus)sion-making contexts. Traditionally, integrating Bayesian principles into models like deep neural networks involves setting priors on parameters and approximating posteriors. This is done despite the fact that, typically, priors on parameters reflect any prior beliefs only insofar as they dictate function space behaviour. In this paper, we rethink this approach and consider what properties characterise a prediction rule as being Bayesian. Algorithms meeting such criteria can be deemed implicitly Bayesian — they make the same predictions as some Bayesian model, without explicitly manifesting priors and posteriors. We argue this might be a more fruitful approach towards integrating Bayesian principles into deep learning. In this paper, we propose how to measure how close a general prediction rule is to being implicitly Bayesian, and empirically evaluate multiple prediction strategies using our approach. We also show theoretically that agents relying on non-implicitly Bayesian prediction rules can be easily exploited in adversarial betting settings.

2024-05-27

approximateinference.org/AABI/2024/Symposium_Archival_Track (accepté)

proceedings.mlr.press

openreview.net

What Mechanisms Does Knowledge Distillation Distill?

Cindy Wu

Ekdeep Singh Lubana

Bruno Mlodozeniec

Robert Kirk

Knowledge distillation is a commonly-used compression method in ML due to the popularity of increasingly large-scale models, but it is uncle… (voir plus)ar if all the information a teacher model contains is distilled into the smaller student model. We aim to formalize the concept of ‘knowledge’ to investigate how knowledge is transferred during distillation, focusing on shared invariant outputs to counterfactual changes of dataset latent variables (we call these latents mechanisms). We define a student model to be a good stand-in model for a teacher if it shares the teacher’s learned mechanisms, and find that Jacobian matching and contrastive representation learning are viable methods by which to train such models. While these methods do not result in perfect transfer of mechanisms, we show they often improve student fidelity or mitigate simplicity bias (as measured by the teacher-to-student KL divergence and accuracy on various out-of-distribution test datasets), especially on datasets with spurious statistical correlations.

2024-05-14

Proceedings of UniReps: the First Workshop on Unifying Representations in Neural Models (publié)

proceedings.mlr.press

openreview.net

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 … (voir 18 de plus)

Heidi Chenyu Zhang

Ruiqi Zhong

Sean O hEigeartaigh

Gabriel Recchia

Giulio Corsi

Alan Chan

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… (voir plus)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 (prépublication)