A Layered Needs-Affordances-Features Approach to Advancing Artificial Intelligence Fairness in Hiring Systems

Summary

Researchers propose a layered needs-affordances-features framework for designing fair AI hiring systems, with a discovery stage that uses theory to find discrimination mechanisms in digital trace data and a utilization stage that turns them into design interventions. Applied to a dataset of 2,506 applicants' interview responses and historical hiring decisions, they identify language-based discrimination and interview question structure as key mechanisms. Modifying linguistic features and increasing interview structure reduced subgroup disparities, with the strongest gains when both were applied jointly.