Tegan Maharaj

Affiliate Member

Tenured Professor, University of Toronto

Biography

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.

Publications

Managing AI Risks in an Era of Rapid Progress

Yoshua Bengio

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

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

2024-05-24

Science (published)

doi.org

arxiv.org

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

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

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)

David Scott Krueger

Tegan Maharaj

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.

2023-06-12

2023 ACM Conference on Fairness, Accountability, and Transparency (published)

doi.org

arxiv.org

Proactive Contact Tracing

Prateek Gupta

Tegan Maharaj

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

David Buckeridge

Chris Pal

Bernhard Schölkopf

Yoshua Bengio

2023-03-13

PLOS Digital Health (published)

doi.org

Factors Influencing Generalization in Chaotic Dynamical Systems

Luã Streit

Vikram Voleti

Tegan Maharaj

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.

2023-02-01

ICLR.cc/2023/Conference (rejected)

openreview.net

Metadata Archaeology: Unearthing Data Subsets by Leveraging Training Dynamics

Shoaib Ahmed Siddiqui

Nitarshan Rajkumar

Tegan Maharaj

David Scott Krueger

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.

2023-02-01

ICLR.cc/2023/Conference (notable)

doi.org

openreview.net

Noisy Pairing and Partial Supervision for Stylized Opinion Summarization

Reinald Kim

Mirella Lapata. 2020

Un-611

David Scott Krueger

Emmanuel Bengio

Maxinder S. Kan-620

Tegan Maharaj

Asja Fischer

Aaron Courville

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

Gelly

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

2023-01-01

(published)

www.semanticscholar.org

Tackling Climate Change with Machine Learning

David Rolnick

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

Tegan Maharaj

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

Yoshua Bengio

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.

2022-02-07

ACM Computing Surveys (published)

doi.org

arxiv.org

Predicting Infectiousness for Proactive Contact Tracing

Yoshua Bengio

Prateek Gupta

Tegan Maharaj

Nasim Rahaman

Martin Weiss

Tristan Deleu

Eilif Benjamin Muller

Meng Qu

Victor Schmidt

Pierre-Luc St-Charles

Hannah Alsdurf

Olexa Bilaniuk

David Buckeridge

gaetan caron

pierre luc carrier

Joumana Ghosn

satya ortiz gagne

Chris Pal

Irina Rish

Bernhard Schölkopf … (see 3 more)

abhinav sharma

Jian Tang

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.

2021-01-12

ICLR.cc/2021/Conference (spotlight)

openreview.net