Our weekly SRI Seminar Series welcomes SRI Director and Chair Gillian Hadfield for a special in-person session at the University of Toronto’s Faculty Club.
The inaugural Schwartz Reisman Chair in Technology and Society and a professor of law and strategic management at the University of Toronto, Hadfield’s research focuses on innovative legal and economic design, AI governance, legal markets, and contract law and theory. In this talk, Hadfield considers how closely automated decision-making systems are able to reproduce forms of human judgement, and identifies a potential failure based in whether the data used to train such systems has been labelled in accordance with either factual or normative claims.
Talk title:
“Judging facts, judging norms: Training ML models to judge humans requires a new approach to labeling data”
Abstract:
As governments and industry turn to increased use of automated decision systems, it becomes essential to consider how closely such systems can reproduce human judgment. We identify a core potential failure. We find that annotators label objects differently depending on whether they are being asked a factual question or a normative question. This challenges a natural assumption maintained in many standard machine-learning (ML) data acquisition procedures: that there is no difference between predicting the factual classification of an object and an exercise of judgment about whether an object violates a rule premised on those facts. We find that using factual labels to train models intended for normative judgments introduces a significant measurement error. We show that models trained using factual labels yield significantly different judgments than those trained using normative labels and that the impact of this effect on model performance can exceed that of other factors (e.g. dataset size) that routinely attract attention from ML researchers and practitioners.
Event schedule
2:30–3:10 PM - Welcome and refreshments (in person)
3:10–4:30 PM - Seminar (in person and online)
4:30–5:30 PM - Post-event reception (in person)
Venue
Faculty Club, University of Toronto, 41 Willcocks St. Toronto ON M5S 3G3
Seminar will be broadcast live via Zoom (register for link).
About Gillian Hadfield
Gillian Hadfield is the inaugural Schwartz Reisman Chair in Technology and Society, director of the Schwartz Reisman Institute for Technology and Society, and a professor of law and strategic management at the University of Toronto. Hadfield’s research is focused on innovative legal and economic design, AI governance, legal markets, and contract law and theory. Her book Rules for a Flat World: Why Humans Invented Law and How to Reinvent It for a Complex Global Economy was published by Oxford University Press in 2017.
Hadfield is a senior policy advisor for OpenAI in San Francisco, and an advisor to courts and several organizations and technology companies engaged in innovating new ways to make law and policy smarter, more accessible, and more responsive to technology and artificial intelligence, including the Hague Institute for Innovation of Law, LegalZoom, and Responsive Law. She was a member of the World Economic Forum’s Future Council for Agile Governance and co-curated their Transformation Map for Justice and Legal Infrastructure; she previously served on the Forum’s Future Council for Technology, Values and Policy and Global Agenda Council for Justice; and was a member of the American Bar Association’s Commission on the Future of Legal Education, and the Dubai Courts of the Future Forum.
About the SRI Seminar Series
The SRI Seminar Series brings together the Schwartz Reisman community and beyond for a robust exchange of ideas that advance scholarship at the intersection of technology and society. Seminars are led by a leading or emerging scholar and feature extensive discussion.
Each week, a featured speaker will present for 45 minutes, followed by an open discussion. Registered attendees will be emailed a Zoom link before the event begins. The event will be recorded and posted online.
Gillian Hadfield