Announcing the inaugural cohort of Schwartz Reisman faculty and graduate fellows
Clockwise from left: Faculty Fellow Karina Vold, Graduate Fellow Shabnam Haghzare, Graduate Fellow Jillian Macklin, and Faculty Fellow Kristen Bos.
The Schwartz Reisman community continues to grow with the appointment of our inaugural group of faculty and graduate fellows from around the University of Toronto’s three campuses.
While the fellows’ areas of research and proposed projects vary widely in scope and subject matter—aligned with SRI’s commitment to fostering broad interdisciplinary work across the sciences, social sciences, and humanities—all align with the four conversations at the core of SRI’s research framework, and the appointed researchers are united in their focus on ensuring powerful new technologies are effective, safe, ethical, and fair.
Our fellows will contribute to the strength and breadth of our research community by fostering multi-disciplinary thinking and collaboration through weekly meetings, talks, workshops, and the mentorship relationships between scholars at different stages of their career.
2020–2021 Faculty Fellows
Kristen Bos is assistant professor of Indigenous science and technology studies in the Historical Studies Department at the University of Toronto Mississauga, and is one of our four faculty fellows.
Bos’s research in environmental health is informed by Indigenous methodologies and Indigenous science and technology studies. Her proposed research project aims to reframe industry-produced data on environmental harms and pollutants by using other data sources (government, community, academic) as necessary counterpoints to place the burden of harms onto polluters themselves.
Bos will be joined by Assistant Professor Aleksandar (Sasho) Nikolov (Department of Computer Science), Assistant Professor Karina Vold (Institute for the History and Philosophy of Science and Technology), and Assistant Professor Nisarg Shah (Department of Computer Science) as faculty fellows.
Faculty Fellows Aleksandar Nikolov (top), and Nisarg Shah (bottom).
Nikolov’s proposed project focuses on differential privacy, a privacy-preservation model by which insights can be extracted from a data set without identifying individual data. While this is an increasingly popular method being adopted across a wide range of industries and sectors, Nikolov aims to address some of its outstanding shortfalls. For example, differential privacy may not easily fit with the mandates of existing privacy legislation and it may be less accurate about small sub-populations, to name just two challenges.
Vold’s proposed project starts with explainable and interpretable AI, which aim to help humans understand otherwise inscrutable machine models, and asks a different but related question: how can humans learn from AI systems such that we can reproduce some of their behaviours without the machine’s presence? Vold works with research in cognitive science and developmental psychology in order to conceptualize how AI might be a tool that assists human cognition rather than threatening to replace it.
Shah’s proposed project aims to add to existing research on participatory budgeting, a democratic process in which residents of a geographical region directly decide how a portion of their public budget is used. His work will focus on three aspects of participatory budgeting methods: discovering optimal ballot design, ensuring fairness, and finding efficient budgetary allocations. Ultimately, Shah’s goal is to find ways to make participatory budgeting as fair and efficient as possible.
2020–2021 Graduate Fellows
Our 15 graduate fellows are a highly interdisciplinary group. This broad cross-section of scholars represents a wide variety of areas of research, from law to anthropology to computer science and beyond.
Clockwise from left: Graduate Fellows Noam Kolt, Suzanne van Geuns, John Enman-Beech, and Elliot Creager.
Elliot Creager’s work in algorithmic bias will examine the ambiguity in both “acceptable” human behaviours and “correct” machine decision rules. By studying how ambiguities are addressed in both fields, he’ll look for new data-driven methods for aligning AI systems to human values. Creager hails from the Department of Computer Science.
John Enman-Beech specializes in contract law within the Faculty of Law, with incorporation of critical feminist legal and economic theory. Their proposed project will examine the ways in which new information technologies complicate well-known problems in contract law.
Suzanne van Geuns, from the Department for the Study of Religion, works on the intersection of religion and digital communications technologies, with a focus on right-wing online subcultures. Her project will locate religion in computational technologies assumed to be secular, with a focus on online “seduction forums” and their imagination of the transformative powers of computational technologies.
Shabnam Haghzare of the Institute of Biomedical Engineering works in human-compatible AI and assistive technologies. Her project will use AI itself to facilitate compatibility between AI-based systems and marginalized groups—in particular, decreasing the mismatch between automated vehicles and their use by older adults, both with and without cognitive impairments.
Noam Kolt’s work in the Faculty of Law focuses on AI and law, and his project will explore the auditing of generative language models in the legal domain. While these models can significantly improve access to justice, their limitations and reliability have not been studied in enough detail to ensure the safe and fair deployment of AI in legal contexts.
Clockwise from left: Graduate Fellows Charlotte Leferink, Daniel Konikoff, and David Madras.
Daniel Konikoff, from the Centre for Criminology and Sociolegal Studies, studies the intersection of criminal justice and technology. His proposed project will look at the social, legal, and ethical ramifications of digital tools used by police for investigation and surveillance—with a particular focus on Canada's risk-driven tracking database and its impact on the criminal justice landscape.
Charlotte Leferink, from the Department of Psychology, compares human visual processing to a type of AI that analyzes visual imagery known as convolutional neural networks (CNNs). Her proposed project will examine how closely the organization of human receptors within high-level visual areas are analogous to distributions in CNNs.
Yang Liu works in sociocultural anthropology in the Department of Anthropology with a focus on algorithms, digital data, and politics. Her project will examine how data-driven approaches in the non-profit sector refashion operational practice and accountable decisions, with a particular focus on how data scientists profile the psychological and behavioral patterns of beneficiaries of social welfare programs.
Jillian Macklin has a background in clinical biochemistry and her current research in the Institute of Health Policy, Management, and Evaluation focuses on equity, patient engagement, and the patient voice in AI-based healthcare tools. Her project will involve co-designing a digital health and/or remote tele-monitoring solution for homeless populations, incorporating human-centred design.
Clockwise, from left: Graduate Fellows Maayan Shvo, Reid McIllroy-Young, and Matthew Marinett.
David Madras’s research into fairness in algorithmic decision-making systems will investigate interactions between algorithmic and human decision-makers, as well as participatory approaches to machine learning design, asking: can we build systems where machine-human partnerships reinforce each other’s strengths? Madras is a member of the Department of Computer Science.
Matthew Marinett’s research in the Faculty of Law focuses on corporate responsibility and accountability in internet content governance (e.g. content moderation). He will develop an accountability framework for online content governance, drawing on principles of accountability from public and administrative law, and their interactions with tech/AI companies.
Reid McIllroy-Young’s work in computational social science and reinforcement learning will aim to model the ways in which human decision-making differs from AI decision-making. Deep learning models trained to play chess can extract generalizable features of the game; by extracting these features from AI systems, can we make deep reinforcement learning systems behave in a more human-like way? McIllroy-Young hails from the Department of Computer Science.
Clockwise, from left: Graduate Fellows Lillio Mok, Evi Micha, and Yang Liu.
Evi Micha’s work at the intersection of computer science and economics will expand research on computational social choice, in both traditional domains like voting, and new domains like ML for ad allocation. Her project will study aggregating individual preferences into collective decisions, taking steps toward an era of interactive democracy. Micha is a member of the Department of Computer Science.
Lillio Mok conducts data-driven social and behavioural research in the Department of Computer Science. His project will focus on user autonomy and well-being online, with a particular focus on algorithms encouraging users to behave in ways they would not otherwise. Mok uses trace data in conjunction with social science techniques to understand situations in which users lose their agency online.
Maayan Shvo’s research at the Department of Computer Science focuses on affective computing (AI that can “understand” human affects). His project will explore whether creating computer systems that can “empathize” could help with what is known as “the alignment problem”—the ideal that an AI’s actions should align with what humans would want. Shvo will collaborate with researchers from related disciplines (e.g., philosophy, psychology) to amass diverse perspectives on an intelligent system’s ability to empathize.
A warm welcome to our inaugural cohort of faculty and graduate fellows. The Schwartz Reisman Institute is pleased to support the creative, innovative, and much-needed work that is being conducted by stellar U of T researchers across all three campuses. We look forward to the invaluable contributions our fellows will make towards deepening our understanding of emerging issues at the intersection of AI, data, and society.
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