Yong Suk Lee

Associate Professor of Technology, Economy, and Global Affairs

Keough School of Global Affairs, University of Notre Dame

Technology Ethics Program Chair, Institute for Ethics and the Common Good

My research examines the economic and societal consequences of technological change, and the governance and policy responses to these transformations.

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News

2026-07-10

ICML Spotlight: There Are Futures That Benchmark-Driven AI Cannot See · ICML 2026

2026-07-10

Paper Accepted at ICML: Predicting AI's Impact on Labor Is a Core Machine Learning Problem · ICML 2026

2026-06-10

New White Paper: AI and the Future of Labor in Korea · Seoul National University

2026-05-20

Cassandra Merritt Presents "Managers as Gatekeepers" at the Wharton Future of Work Conference · The Wharton School, Philadelphia

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Research Themes

Labor Market Impacts

Measuring and understanding how AI and robotics reshape employment, wages, and the structure of work — from occupational exposure scores and CPS analysis to facility-level studies of robot adoption.

Studying how workers, managers, and organizations actually engage with AI — the gap between capability and adoption, how AI changes decision-making, and what happens when humans and algorithms collaborate.

Beliefs, Policy & Governance

Investigating how people's beliefs about technology shape behavior and policy preferences — and developing evidence-based frameworks for AI governance, workforce policy, and responsible innovation.

Entrepreneurship & Innovation

Examining how institutions, education, and policy shape entrepreneurial activity and innovation — from university programs and government-backed lending to the geography of small business growth.

Aging & Demographic Change

Understanding how population aging and demographic shifts interact with technology, labor markets, and care systems — from robot adoption in elder care to staffing and quality in nursing homes.

Recent Work

2026

Predicting AI's Impact on Labor Is a Core Machine Learning Problem · International Conference on Machine Learning

2026

There Are Futures That Benchmark-Driven AI Cannot See · with S. Lotfi, A. Iranmanesh, L. Naghashyar, A. Shirali, F. Nateghi Haredasht, S. Koyejo, P. Torr, F. Barez, J. Lehman, P. Norvig, A. Narayanan · International Conference on Machine Learning

2025

Do Multimodal Large Language Models Understand Welding? · with N. Chawla, G. Khvatskii, C. Angst, M. Gibbs, R. Landers · Information Fusion