Publications

Open Problems in Technical AI Governance

Anka Reuel

Benjamin Bucknall

Stephen Casper

Timothy Fist

Lisa Soder

Onni Aarne

Lewis Hammond

Lujain Ibrahim

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)

doi.org

openreview.net

Predicting College Enrollment for Low-Socioeconomic-Status Students Using Machine Learning Approaches

Surina He

Mehrdad Yousefpoori-Naeim

Ying Cui

Maria Cutumisu

2025-04-12

Big Data and Cognitive Computing (published)

doi.org

Semantic Commit: Helping Users Update Intent Specifications for AI Memory at Scale

Priyan Vaithilingam

Munyeong Kim

Frida-Cecilia Acosta-Parenteau

Daniel Lee

Amine Mhedhbi

Elena L. Glassman

Ian Arawjo

2025-04-12

ArXiv (preprint)

arxiv.org

Semantic Commit: Helping Users Update Intent Specifications for AI Memory at Scale

Priyan Vaithilingam

Munyeong Kim

Frida-Cecilia Acosta-Parenteau

Daniel Lee

Amine Mhedhbi

Elena L. Glassman

Ian Arawjo

2025-04-12

ArXiv (preprint)

arxiv.org

BindGPT: A Scalable Framework for 3D Molecular Design via Language Modeling and Reinforcement Learning

Artem Zholus

Maksim Kuznetsov

Roman Schutski

Shayakhmetov Rim

Daniil Polykovskiy

Sarath Chandar

Alex Zhavoronkov

Generating novel active molecules for a given protein is an extremely challenging task for generative models that requires an understanding … (see more)of the complex physical interactions between the molecule and its environment. In this paper, we present a novel generative model, BindGPT which uses a conceptually simple but powerful approach to create 3D molecules within the protein's binding site. Our model produces molecular graphs and conformations jointly, eliminating the need for an extra graph reconstruction step. We pretrain BindGPT on a large-scale dataset and fine-tune it with reinforcement learning using scores from external simulation software. We demonstrate how a single pretrained language model can serve at the same time as a 3D molecular generative model, conformer generator conditioned on the molecular graph, and a pocket-conditioned 3D molecule generator. Notably, the model does not make any representational equivariance assumptions about the domain of generation. We show how such simple conceptual approach combined with pretraining and scaling can perform on par or better than the current best specialized diffusion models, language models, and graph neural networks while being two orders of magnitude cheaper to sample.

2025-04-11

Proceedings of the AAAI Conference on Artificial Intelligence (published)

doi.org

arxiv.org

FoMo: Multi-Modal, Multi-Scale and Multi-Task Remote Sensing Foundation Models for Forest Monitoring

Nikolaos Ioannis Bountos

Arthur Ouaknine

Ioannis Papoutsis

David Rolnick

2025-04-11

Proceedings of the AAAI Conference on Artificial Intelligence (published)

doi.org

arxiv.org

Genetic modulation of brain dynamics in neurodevelopmental disorders: the impact of copy number variations on resting-state EEG

Adrien Dubois

Elisabeth Audet-Duchesne

Inga Sophia Knoth

Charles-Olivier Martin

Khadije Jizi

Petra Tamer

Nadine Younis

Sébastien Jacquemont

Guillaume Dumas

Sarah Lippé

2025-04-11

Translational Psychiatry (published)

doi.org

A Layer Selection Approach to Test Time Adaptation

Sabyasachi Sahoo

Mostafa ElAraby

Jonas Ngnawe

Yann Batiste Pequignot

Frederic Precioso

Christian Gagné

Test Time Adaptation (TTA) addresses the problem of distribution shift by adapting a pretrained model to a new domain during inference. When… (see more) faced with challenging shifts, most methods collapse and perform worse than the original pretrained model. In this paper, we find that not all layers are equally receptive to the adaptation, and the layers with the most misaligned gradients often cause performance degradation. To address this, we propose GALA, a novel layer selection criterion to identify the most beneficial updates to perform during test time adaptation. This criterion can also filter out unreliable samples with noisy gradients. Its simplicity allows seamless integration with existing TTA loss functions, thereby preventing degradation and focusing adaptation on the most trainable layers. This approach also helps to regularize adaptation to preserve the pretrained features, which are crucial for handling unseen domains. Through extensive experiments, we demonstrate that the proposed layer selection framework improves the performance of existing TTA approaches across multiple datasets, domain shifts, model architectures, and TTA losses.

2025-04-11

Proceedings of the AAAI Conference on Artificial Intelligence (published)

doi.org

openreview.net

StarVector: Generating Scalable Vector Graphics Code from Images and Text

Juan A. Rodriguez

Abhay Puri

Shubham Agarwal

Issam Hadj Laradji

Pau Rodriguez

Sai Rajeswar

David Vazquez