Portrait of Tegan Maharaj

Tegan Maharaj

Affiliate Member
Tenured Professor, University of Toronto


I am an assistant professor in the University of Toronto’s Faculty of Information.

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.


Managing AI Risks in an Era of Rapid Progress
Geoffrey Hinton
Andrew Yao
Dawn Song
Pieter Abbeel
Yuval Noah Harari
Trevor Darrell
Ya-Qin Zhang
Lan Xue
Shai Shalev-Shwartz
Gillian K. Hadfield
Jeff Clune
Frank Hutter
Atilim Güneş Baydin
Sheila McIlraith
Qiqi Gao
Ashwin Acharya
Anca Dragan … (see 5 more)
Philip Torr
Stuart Russell
Daniel Kahneman
Jan Brauner
Sören Mindermann
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 Gunther
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
Beyond Predictive Algorithms in Child Welfare
Erina Seh-Young Moon
Erin Moon
Devansh Saxena
Shion Guha
Harms from Increasingly Agentic Algorithmic Systems
Alan Chan
Rebecca Salganik
Alva Markelius
Chris Pang
Nitarshan Rajkumar
Dmitrii Krasheninnikov
Lauro Langosco
Zhonghao He
Yawen Duan
Micah Carroll
Michelle Lin
Alex Mayhew
Katherine Collins
Maryam Molamohammadi
John Burden
Wanru Zhao
Shalaleh Rismani
Konstantinos Voudouris
Umang Bhatt
Adrian Weller … (see 2 more)
Research in Fairness, Accountability, Transparency, and Ethics (FATE)1 has established many sources and forms of algorithmic harm, in domain… (see more)s as diverse as health care, finance, policing, and recommendations. Much work remains to be done to mitigate the serious harms of these systems, particularly those disproportionately affecting marginalized communities. Despite these ongoing harms, new systems are being developed and deployed, typically without strong regulatory barriers, threatening the perpetuation of the same harms and the creation of novel ones. In response, the FATE community has emphasized the importance of anticipating harms, rather than just responding to them. Anticipation of harms is especially important given the rapid pace of developments in machine learning (ML). Our work focuses on the anticipation of harms from increasingly agentic systems. Rather than providing a definition of agency as a binary property, we identify 4 key characteristics which, particularly in combination, tend to increase the agency of a given algorithmic system: underspecification, directness of impact, goal-directedness, and long-term planning. We also discuss important harms which arise from increasing agency – notably, these include systemic and/or long-range impacts, often on marginalized or unconsidered stakeholders. We emphasize that recognizing agency of algorithmic systems does not absolve or shift the human responsibility for algorithmic harms. Rather, we use the term agency to highlight the increasingly evident fact that ML systems are not fully under human control. Our work explores increasingly agentic algorithmic systems in three parts. First, we explain the notion of an increase in agency for algorithmic systems in the context of diverse perspectives on agency across disciplines. Second, we argue for the need to anticipate harms from increasingly agentic systems. Third, we discuss important harms from increasingly agentic systems and ways forward for addressing them. We conclude by reflecting on implications of our work for anticipating algorithmic harms from emerging systems.
Proactive Contact Tracing
Prateek Gupta
Martin Weiss
Nasim Rahaman
Hannah Alsdurf
Nanor Minoyan
Soren Harnois-Leblanc
Joanna Merckx
andrew williams
Victor Schmidt
Pierre-Luc St-Charles
Akshay Patel
Yang Zhang
Bernhard Schölkopf
Factors Influencing Generalization in Chaotic Dynamical Systems
Luã Streit
Vikram Voleti
Many real-world systems exhibit chaotic behaviour, for example: weather, fluid dynamics, stock markets, natural ecosystems, and disease tran… (see more)smission. While chaotic systems are often thought to be completely unpredictable, in fact there are patterns within and across that experts frequently describe and contrast qualitatively. We hypothesize that given the right supervision / task definition, representation learning systems will be able to pick up on these patterns, and successfully generalize both in- and out-of-distribution (OOD). Thus, this work explores and identifies key factors which lead to good generalization. We observe a variety of interesting phenomena, including: learned representations transfer much better when fine-tuned vs. frozen; forecasting appears to be the best pre-training task; OOD robustness falls off very quickly outside the training distribution; recurrent architectures generally outperform others on OOD generalization. Our findings are of interest to any domain of prediction where chaotic dynamics play a role.
Metadata Archaeology: Unearthing Data Subsets by Leveraging Training Dynamics
Shoaib Ahmed Siddiqui
Nitarshan Rajkumar
Sara Hooker
Modern machine learning research relies on relatively few carefully curated datasets. Even in these datasets, and typically in `untidy' or r… (see more)aw data, practitioners are faced with significant issues of data quality and diversity which can be prohibitively labor intensive to address. Existing methods for dealing with these challenges tend to make strong assumptions about the particular issues at play, and often require a priori knowledge or metadata such as domain labels. Our work is orthogonal to these methods: we instead focus on providing a unified and efficient framework for Metadata Archaeology -- uncovering and inferring metadata of examples in a dataset. We curate different subsets of data that might exist in a dataset (e.g. mislabeled, atypical, or out-of-distribution examples) using simple transformations, and leverage differences in learning dynamics between these probe suites to infer metadata of interest. Our method is on par with far more sophisticated mitigation methods across different tasks: identifying and correcting mislabeled examples, classifying minority-group samples, prioritizing points relevant for training and enabling scalable human auditing of relevant examples.
Noisy Pairing and Partial Supervision for Stylized Opinion Summarization
Reinald Kim
Mirella Lapata. 2020
Emmanuel Bengio
Maxinder S. Kan-620
Asja Fischer
Somnath Basu
Roy Chowdhury
Chao Zhao
Tanya Goyal
Junyi Jiacheng Xu
Jessy Li
Ivor Wai-hung Tsang
James T. Kwok
Neil Houlsby
Andrei Giurgiu
Stanisław Jastrzębski … (see 22 more)
Bruna Morrone
Quentin de Laroussilhe
Mona Gesmundo
Attariyan Sylvain
Thomas Wolf
Lysandre Debut
Julien Victor Sanh
Clement Chaumond
Anthony Delangue
Pier-339 Moi
Tim ric Cistac
R´emi Rault
Morgan Louf
Funtow-900 Joe
Sam Davison
Patrick Shleifer
Von Platen
Clara Ma
Yacine Jernite
Julien Plu
Canwen Xu
Opinion summarization research has primar-001 ily focused on generating summaries reflect-002 ing important opinions from customer reviews 0… (see more)03 without paying much attention to the writing 004 style. In this paper, we propose the stylized 005 opinion summarization task, which aims to 006 generate a summary of customer reviews in 007 the desired (e.g., professional) writing style. 008 To tackle the difficulty in collecting customer 009 and professional review pairs, we develop a 010 non-parallel training framework, Noisy Pair-011 ing and Partial Supervision ( NAPA ), which 012 trains a stylized opinion summarization sys-013 tem from non-parallel customer and profes-014 sional review sets. We create a benchmark P RO - 015 S UM by collecting customer and professional 016 reviews from Yelp and Michelin. Experimental 017 results on P RO S UM and FewSum demonstrate 018 that our non-parallel training framework con-019 sistently improves both automatic and human 020 evaluations, successfully building a stylized 021 opinion summarization model that can gener-022 ate professionally-written summaries from cus-023 tomer reviews. 024
Tackling Climate Change with Machine Learning
Priya L. Donti
Lynn H. Kaack
Kelly Kochanski
Alexandre Lacoste
Kris Sankaran
Andrew Slavin Ross
Nikola Milojevic-Dupont
Natasha Jaques
Anna Waldman-Brown
Alexandra Luccioni
Evan David Sherwin
S. Karthik Mukkavilli
Konrad Paul Kording
Carla P. Gomes
Andrew Y. Ng
Demis Hassabis
John C. Platt
Felix Creutzig … (see 2 more)
Jennifer T Chayes
Climate change is one of the greatest challenges facing humanity, and we, as machine learning (ML) experts, may wonder how we can help. Here… (see more) we describe how ML can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by ML, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the ML community to join the global effort against climate change.
Predicting Infectiousness for Proactive Contact Tracing
Prateek Gupta
Nasim Rahaman
Martin Weiss
Tristan Deleu
Meng Qu
Victor Schmidt
Pierre-Luc St-Charles
Hannah Alsdurf
Olexa Bilaniuk
gaetan caron
pierre luc carrier
Joumana Ghosn
satya ortiz gagne
Bernhard Schölkopf … (see 3 more)
abhinav sharma
andrew williams
The COVID-19 pandemic has spread rapidly worldwide, overwhelming manual contact tracing in many countries and resulting in widespread lockdo… (see more)wns for emergency containment. Large-scale digital contact tracing (DCT) has emerged as a potential solution to resume economic and social activity while minimizing spread of the virus. Various DCT methods have been proposed, each making trade-offs between privacy, mobility restrictions, and public health. The most common approach, binary contact tracing (BCT), models infection as a binary event, informed only by an individual's test results, with corresponding binary recommendations that either all or none of the individual's contacts quarantine. BCT ignores the inherent uncertainty in contacts and the infection process, which could be used to tailor messaging to high-risk individuals, and prompt proactive testing or earlier warnings. It also does not make use of observations such as symptoms or pre-existing medical conditions, which could be used to make more accurate infectiousness predictions. In this paper, we use a recently-proposed COVID-19 epidemiological simulator to develop and test methods that can be deployed to a smartphone to locally and proactively predict an individual's infectiousness (risk of infecting others) based on their contact history and other information, while respecting strong privacy constraints. Predictions are used to provide personalized recommendations to the individual via an app, as well as to send anonymized messages to the individual's contacts, who use this information to better predict their own infectiousness, an approach we call proactive contact tracing (PCT). We find a deep-learning based PCT method which improves over BCT for equivalent average mobility, suggesting PCT could help in safe re-opening and second-wave prevention.
COVI-AgentSim: an Agent-based Model for Evaluating Methods of Digital Contact Tracing
Prateek Gupta
Martin Weiss
Nasim Rahaman
Hannah Alsdurf
abhinav sharma
Nanor Minoyan
Soren Harnois-Leblanc
Victor Schmidt
Pierre-Luc St-Charles
Tristan Deleu
andrew williams
Akshay Patel
Meng Qu
Olexa Bilaniuk
gaetan caron
pierre luc carrier
satya ortiz gagne
Marc-Andre Rousseau
Joumana Ghosn
Yang Zhang
Bernhard Schölkopf
Joanna Merckx
COVI White Paper
Hannah Alsdurf
Tristan Deleu
Prateek Gupta
Daphne Ippolito
Richard Janda
Max Jarvie
Tyler J. Kolody
Sekoul Krastev
Robert Obryk
Dan Pilat
Valerie Pisano
Benjamin Prud'homme
Meng Qu
Nasim Rahaman
Jean-franois Rousseau
abhinav sharma
Brooke Struck … (see 3 more)
Martin Weiss
Yun William Yu