CHDS Seminar with Jann Spiess: On the Fairness of Machine-Assisted Human Decisions

Abstract:  When machine-learning algorithms are deployed in high-stakes decisions, we want to ensure that their deployment leads to fair outcomes. This concern has motivated a fast-growing literature that focuses on disparities in machine predictions. However, many algorithms are deployed to assist rather than replace human decision-makers. In this project, we analyze the fairness of algorithm-assisted human decisions in a formal model and in a lab experiment. We show that the inclusion of a biased human decision-maker can revert common relationships between the structure of the algorithm and the qualities of resulting decisions.

Bio: Jann Spiess is an econometrician at Stanford University who works on integrating techniques and insights from machine learning into the econometric toolbox. His recent work focuses on (1) building robust tools for causal inference, (2) modeling data-driven decisions with conflicts of interest, including the design of pre-analysis plans, algorithmic fairness, and AI regulation, and (3) understanding human–AI interaction.  He holds a PhD in economics from Harvard University and a master’s degree in public policy from the Harvard Kennedy School. His background is in mathematics with a focus on probability theory and combinatorics.