Portrait of David Scott Krueger

David Scott Krueger

Core Academic Member
Assistant professor, Université de Montréal, Department of Computer Science and Operations Research (DIRO)
Research Topics
Deep Learning
Representation Learning

Biography

David Krueger is an Assistant Professor in Robust, Reasoning and Responsible AI in the Department of Computer Science and Operations Research (DIRO) at University of Montreal, and a Core Academic Member at Mila - Quebec Artificial Intelligence Institute, UC Berkeley's Center for Human-Compatible AI (CHAI), and the Center for the Study of Existential Risk (CSER). His work focuses on reducing the risk of human extinction from artificial intelligence (AI x-risk) through technical research as well as education, outreach, governance and advocacy.

His research spans many areas of Deep Learning, AI Alignment, AI Safety and AI Ethics, including alignment failure modes, algorithmic manipulation, interpretability, robustness, and understanding how AI systems learn and generalize. He has been featured in media outlets including ITV's Good Morning Britain, Al Jazeera's Inside Story, France 24, New Scientist and the Associated Press.

David completed his graduate studies at the University of Montreal and Mila - Quebec Artificial Intelligence Institute, working with Yoshua Bengio, Roland Memisevic, and Aaron Courville.

Current Students

PhD - Université de Montréal
Principal supervisor :
Collaborating researcher

Publications

IDs for AI Systems
Alan Chan
Noam Kolt
Peter Wills
Usman Anwar
Christian Schroeder de Witt
Nitarshan Rajkumar
Lewis Hammond
Lennart Heim
Markus Anderljung
IDs for AI Systems
Alan Chan
Noam Kolt
Peter Wills
Usman Anwar
Christian Schroeder de Witt
Nitarshan Rajkumar
Lewis Hammond
Lennart Heim
Markus Anderljung
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
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.
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
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… (see more)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.
Stress-Testing Capability Elicitation With Password-Locked Models
Ryan Greenblatt
Fabien Roger
Dmitrii Krasheninnikov
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… (see more)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.
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… (see more)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.
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 Chenyu Zhang
Ruiqi Zhong
Sean O hEigeartaigh
Gabriel Recchia
Giulio Corsi
Alan Chan
Markus Anderljung
Lilian Edwards
Danqi Chen
Samuel Albanie
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
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 Chenyu Zhang
Ruiqi Zhong
Sean O hEigeartaigh
Gabriel Recchia
Giulio Corsi
Alan Chan
Markus Anderljung
Lilian Edwards
Danqi Chen
Samuel Albanie
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
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 Chenyu Zhang
Ruiqi Zhong
Sean O hEigeartaigh
Gabriel Recchia
Giulio Corsi
Alan Chan
Markus Anderljung
Lilian Edwards
Danqi Chen
Samuel Albanie
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
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
Alan Chan
Markus Anderljung
Lilian Edwards
Danqi Chen
Samuel Albanie
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