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

Yong Suk Lee's position paper, "Predicting AI's Impact on Labor Is a Core Machine Learning Problem," has been accepted at the International Conference on Machine Learning (ICML) 2026.

The paper argues that predicting AI's impact on labor should be treated as a core machine learning problem — one that the AI and ML community has a distinctive role in shaping — rather than solely a societal or ethical question. This prediction task sits at the center of modern ML: prediction under non-stationarity, distribution shift, endogenous feedback, and high-stakes uncertainty. The paper discusses key prediction targets across units of analysis and time horizons, reviews current approaches in economics, management, and ML, identifies technical obstacles that limit existing methods, and proposes a research agenda for ML-driven labor prediction.