Portrait of Tegan Maharaj

Tegan Maharaj

Core Academic Member
tegan.maharaj@mila.quebec
Assistant Professor in Machine Learning, HEC Montréal, Department of Decision Science
Research Topics
Deep Learning
Dynamical Systems
Machine Learning Theory
Multimodal Learning
Representation Learning
Website
Google Scholar

Biography

I am an assistant professor at the Department of Decision Science at HEC Montréal.

The goal of my research is to contribute understanding and techniques to the growing science of responsible AI development, while usefully applying AI to high-impact ecological problems related to climate change, epidemiology, AI alignment and ecological impact assessments. My recent research has two themes: (1) using deep models for policy analysis and risk mitigation, and (2) designing data or unit test environments to empirically evaluate learning behaviour or simulate the deployment of AI systems. Please contact me if you are interested in collaborations in these areas.

I am generally interested in studying “what goes into” deep models—not only data, but also the broader learning environment (e.g., task design/specification, loss function and regularization) and the broader societal context of deployment (e.g., privacy considerations, trends and incentives, norms and human biases). I am concerned and passionate about AI ethics and safety, and the application of ML to environmental management, health and social welfare.

Current Students

Carol Altimas
Master's Research - Université de Montréal
Principal supervisor :
Étienne Laliberté

Publications

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
Yoshua Bengio
Danqi Chen
Samuel Albanie
Tegan Maharaj
Jakob Nicolaus Foerster
Florian Tramèr
He He
Atoosa Kasirzadeh
Yejin Choi
David Scott Krueger
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)
doi.org
arxiv.org
Beyond Predictive Algorithms in Child Welfare
Erina Seh-Young Moon
Erin Moon
Devansh Saxena
Tegan Maharaj
Shion Guha
2024-01-23
graphicsinterface.org/Graphics_Interface/2024/Conference (published)
doi.org
openreview.net
Managing extreme AI risks amid rapid progress
Yoshua Bengio
Geoffrey Hinton
Andrew Yao
Dawn Song
Pieter Abbeel
Trevor Darrell
Yuval Noah Harari
Ya-Qin Zhang
Lan Xue
Shai Shalev-Shwartz
Gillian K. Hadfield
Jeff Clune
Tegan Maharaj
Frank Hutter
Atilim Güneş Baydin
Sheila McIlraith
Qiqi Gao
Ashwin Acharya
David Scott Krueger
Anca Dragan … (see 5 more)
Philip Torr
Stuart Russell
Daniel Kahneman
Jan Brauner
Sören Mindermann
Preparation requires technical research and development, as well as adaptive, proactive governance Artificial intelligence (AI) is progressi… (see more)ng rapidly, and companies are shifting their focus to developing generalist AI systems that can autonomously act and pursue goals. Increases in capabilities and autonomy may soon massively amplify AI’s impact, with risks that include large-scale social harms, malicious uses, and an irreversible loss of human control over autonomous AI systems. Although researchers have warned of extreme risks from AI (1), there is a lack of consensus about how to manage them. Society’s response, despite promising first steps, is incommensurate with the possibility of rapid, transformative progress that is expected by many experts. AI safety research is lagging. Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness and barely address autonomous systems. Drawing on lessons learned from other safety-critical technologies, we outline a comprehensive plan that combines technical research and development (R&D) with proactive, adaptive governance mechanisms for a more commensurate preparation.
2023-10-26
Science (published)
doi.org
Managing extreme AI risks amid rapid progress
Yoshua Bengio
Geoffrey Hinton
Andrew Yao
Dawn Song
Pieter Abbeel
Trevor Darrell
Yuval Noah Harari
Ya-Qin Zhang
Lan Xue
Shai Shalev-Shwartz
Gillian K. Hadfield
Jeff Clune
Tegan Maharaj
Frank Hutter
Atilim Güneş Baydin
Sheila McIlraith
Qiqi Gao
Ashwin Acharya
David Scott Krueger
Anca Dragan … (see 5 more)
Philip Torr
Stuart Russell
Daniel Kahneman
Jan Brauner
Sören Mindermann
Preparation requires technical research and development, as well as adaptive, proactive governance Artificial intelligence (AI) is progressi… (see more)ng rapidly, and companies are shifting their focus to developing generalist AI systems that can autonomously act and pursue goals. Increases in capabilities and autonomy may soon massively amplify AI’s impact, with risks that include large-scale social harms, malicious uses, and an irreversible loss of human control over autonomous AI systems. Although researchers have warned of extreme risks from AI (1), there is a lack of consensus about how to manage them. Society’s response, despite promising first steps, is incommensurate with the possibility of rapid, transformative progress that is expected by many experts. AI safety research is lagging. Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness and barely address autonomous systems. Drawing on lessons learned from other safety-critical technologies, we outline a comprehensive plan that combines technical research and development (R&D) with proactive, adaptive governance mechanisms for a more commensurate preparation.
2023-10-26
Science (published)
doi.org
Managing extreme AI risks amid rapid progress
Yoshua Bengio
Geoffrey Hinton
Andrew Yao
Dawn Song
Pieter Abbeel
Trevor Darrell
Yuval Noah Harari
Ya-Qin Zhang
Lan Xue
Shai Shalev-Shwartz
Gillian K. Hadfield
Jeff Clune
Tegan Maharaj
Frank Hutter
Atilim Güneş Baydin
Sheila McIlraith
Qiqi Gao
Ashwin Acharya
David Scott Krueger
Anca Dragan … (see 5 more)
Philip Torr
Stuart Russell
Daniel Kahneman
Jan Brauner
Sören Mindermann
Preparation requires technical research and development, as well as adaptive, proactive governance Artificial intelligence (AI) is progressi… (see more)ng rapidly, and companies are shifting their focus to developing generalist AI systems that can autonomously act and pursue goals. Increases in capabilities and autonomy may soon massively amplify AI’s impact, with risks that include large-scale social harms, malicious uses, and an irreversible loss of human control over autonomous AI systems. Although researchers have warned of extreme risks from AI (1), there is a lack of consensus about how to manage them. Society’s response, despite promising first steps, is incommensurate with the possibility of rapid, transformative progress that is expected by many experts. AI safety research is lagging. Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness and barely address autonomous systems. Drawing on lessons learned from other safety-critical technologies, we outline a comprehensive plan that combines technical research and development (R&D) with proactive, adaptive governance mechanisms for a more commensurate preparation.
2023-10-26
Science (published)
doi.org
Managing extreme AI risks amid rapid progress
Yoshua Bengio
Geoffrey Hinton
Andrew Yao
Dawn Song
Pieter Abbeel
Trevor Darrell
Yuval Noah Harari
Ya-Qin Zhang
Lan Xue
Shai Shalev-Shwartz
Gillian K. Hadfield
Jeff Clune
Tegan Maharaj
Frank Hutter
Atilim Güneş Baydin
Sheila McIlraith
Qiqi Gao
Ashwin Acharya
David Scott Krueger
Anca Dragan … (see 5 more)
Philip Torr
Stuart Russell
Daniel Kahneman
Jan Brauner
Sören Mindermann
Preparation requires technical research and development, as well as adaptive, proactive governance Artificial intelligence (AI) is progressi… (see more)ng rapidly, and companies are shifting their focus to developing generalist AI systems that can autonomously act and pursue goals. Increases in capabilities and autonomy may soon massively amplify AI’s impact, with risks that include large-scale social harms, malicious uses, and an irreversible loss of human control over autonomous AI systems. Although researchers have warned of extreme risks from AI (1), there is a lack of consensus about how to manage them. Society’s response, despite promising first steps, is incommensurate with the possibility of rapid, transformative progress that is expected by many experts. AI safety research is lagging. Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness and barely address autonomous systems. Drawing on lessons learned from other safety-critical technologies, we outline a comprehensive plan that combines technical research and development (R&D) with proactive, adaptive governance mechanisms for a more commensurate preparation.
2023-10-26
Science (published)
doi.org
Managing extreme AI risks amid rapid progress
Yoshua Bengio
Geoffrey Hinton
Andrew Yao
Dawn Song
Pieter Abbeel
Trevor Darrell
Yuval Noah Harari
Ya-Qin Zhang
Lan Xue
Shai Shalev-Shwartz
Gillian K. Hadfield
Jeff Clune
Tegan Maharaj
Frank Hutter
Atilim Güneş Baydin
Sheila McIlraith
Qiqi Gao
Ashwin Acharya
David Scott Krueger
Anca Dragan … (see 5 more)
Philip Torr
Stuart Russell
Daniel Kahneman
Jan Brauner
Sören Mindermann
Preparation requires technical research and development, as well as adaptive, proactive governance Artificial intelligence (AI) is progressi… (see more)ng rapidly, and companies are shifting their focus to developing generalist AI systems that can autonomously act and pursue goals. Increases in capabilities and autonomy may soon massively amplify AI’s impact, with risks that include large-scale social harms, malicious uses, and an irreversible loss of human control over autonomous AI systems. Although researchers have warned of extreme risks from AI (1), there is a lack of consensus about how to manage them. Society’s response, despite promising first steps, is incommensurate with the possibility of rapid, transformative progress that is expected by many experts. AI safety research is lagging. Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness and barely address autonomous systems. Drawing on lessons learned from other safety-critical technologies, we outline a comprehensive plan that combines technical research and development (R&D) with proactive, adaptive governance mechanisms for a more commensurate preparation.
2023-10-26
Science (published)
doi.org
Managing extreme AI risks amid rapid progress
Yoshua Bengio
Geoffrey Hinton
Andrew Yao
Dawn Song
Pieter Abbeel
Trevor Darrell
Yuval Noah Harari
Ya-Qin Zhang
Lan Xue
Shai Shalev-Shwartz
Gillian K. Hadfield
Jeff Clune
Tegan Maharaj
Frank Hutter
Atilim Güneş Baydin
Sheila McIlraith
Qiqi Gao
Ashwin Acharya
David Scott Krueger
Anca Dragan … (see 5 more)
Philip Torr
Stuart Russell
Daniel Kahneman
Jan Brauner
Sören Mindermann
2023-10-26
Science (published)
doi.org
Managing extreme AI risks amid rapid progress
Yoshua Bengio
Geoffrey Hinton
Andrew Yao
Dawn Song
Pieter Abbeel
Trevor Darrell
Yuval Noah Harari
Ya-Qin Zhang
Lan Xue
Shai Shalev-Shwartz
Gillian K. Hadfield
Jeff Clune
Tegan Maharaj
Frank Hutter
Atilim Güneş Baydin
Sheila McIlraith
Qiqi Gao
Ashwin Acharya
David Scott Krueger
Anca Dragan … (see 5 more)
Philip Torr
Stuart Russell
Daniel Kahneman
Jan Brauner
Sören Mindermann
Preparation requires technical research and development, as well as adaptive, proactive governance Artificial intelligence (AI) is progressi… (see more)ng rapidly, and companies are shifting their focus to developing generalist AI systems that can autonomously act and pursue goals. Increases in capabilities and autonomy may soon massively amplify AI’s impact, with risks that include large-scale social harms, malicious uses, and an irreversible loss of human control over autonomous AI systems. Although researchers have warned of extreme risks from AI (1), there is a lack of consensus about how to manage them. Society’s response, despite promising first steps, is incommensurate with the possibility of rapid, transformative progress that is expected by many experts. AI safety research is lagging. Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness and barely address autonomous systems. Drawing on lessons learned from other safety-critical technologies, we outline a comprehensive plan that combines technical research and development (R&D) with proactive, adaptive governance mechanisms for a more commensurate preparation.
2023-10-26
Science (published)
doi.org
Managing extreme AI risks amid rapid progress
Yoshua Bengio
Geoffrey Hinton
Andrew Yao
Dawn Song
Pieter Abbeel
Trevor Darrell
Yuval Noah Harari
Ya-Qin Zhang
Lan Xue
Shai Shalev-Shwartz
Gillian K. Hadfield
Jeff Clune
Tegan Maharaj
Frank Hutter
Atilim Güneş Baydin
Sheila McIlraith
Qiqi Gao
Ashwin Acharya
David Scott Krueger
Anca Dragan … (see 5 more)
Philip Torr
Stuart Russell
Daniel Kahneman
Jan Brauner
Sören Mindermann
2023-10-26
Science (published)
doi.org
Managing AI Risks in an Era of Rapid Progress
Yoshua Bengio
Geoffrey Hinton
Andrew Yao
Dawn Song
Pieter Abbeel
Yuval Noah Harari
Ya-Qin Zhang
Lan Xue
Shai Shalev-Shwartz
Gillian K. Hadfield
Jeff Clune
Tegan Maharaj
Frank Hutter
Atilim Güneş Baydin
Sheila McIlraith
Qiqi Gao
Ashwin Acharya
David Scott Krueger
Anca Dragan
Philip Torr … (see 4 more)
Stuart Russell
Daniel Kahneman
Jan Brauner
Sören Mindermann
In this short consensus paper, we outline risks from upcoming, advanced AI systems. We examine large-scale social harms and malicious uses, … (see more)as well as an irreversible loss of human control over autonomous AI systems. In light of rapid and continuing AI progress, we propose priorities for AI R&D and governance.
2023-10-26
ArXiv (preprint)
doi.org
arxiv.org
Managing AI Risks in an Era of Rapid Progress
Yoshua Bengio
Geoffrey Hinton
Andrew Yao
Dawn Song
Pieter Abbeel
Yuval Noah Harari
Ya-Qin Zhang
Lan Xue
Shai Shalev-Shwartz
Gillian K. Hadfield
Jeff Clune
Tegan Maharaj
Frank Hutter
Atilim Güneş Baydin
Sheila McIlraith
Qiqi Gao
Ashwin Acharya
David Scott Krueger
Anca Dragan
Philip Torr … (see 4 more)
Stuart Russell
Daniel Kahneman
Jan Brauner
Sören Mindermann
In this short consensus paper, we outline risks from upcoming, advanced AI systems. We examine large-scale social harms and malicious uses, … (see more)as well as an irreversible loss of human control over autonomous AI systems. In light of rapid and continuing AI progress, we propose priorities for AI R&D and governance.
2023-10-26
ArXiv (preprint)
doi.org
arxiv.org