Date: Wednesday, June 1st, 2022
9:00 am – 10:00 am Pacific Time
12:00 pm – 1:00 pm Eastern Time
Location: Weekly Seminar, Zoom
Title: Multi Stage Screening: Enforcing Fairness and Maximizing Efficiency in a Pre-Existing Pipeline
Consider an actor making selection decisions using a series of classifiers, which we term a sequential screening process. The early stages filter out some applicants, and in the final stage an expensive but accurate test is applied to the individuals that make it to the final stage. Since the final stage is expensive, if there are multiple groups with different fractions of positives at the penultimate stage (even if a slight gap), then the firm may naturally only choose to the apply the final (interview) stage solely to the highest precision group which would be clearly unfair to the other groups. We consider requiring Equality of Opportunity (qualified individuals from each group have the same chance of reaching the final stage and being interviewed). We then examine the goal of maximizing quantities of interest to the decision maker subject to this constraint, via modification of the probabilities of promotion through the screening process at each stage based on performance at the previous stage. We exhibit algorithms for satisfying Equal Opportunity over the selection process and maximizing precision as well as linear combinations of precision and recall at the end of the final stage.
Kevin Stangl is a Phd candidate at the Toyota Technological Institute at Chicago, advised by Professor Avrim Blum, and his research focuses on fairness in machine learning and learning theory.