Active Fairness in Algorithmic Decision Making

Understand

Society increasingly relies on machine learning models for automated decision making.

• Yet, efficiency gains from automation have come paired with concern for algorithmic discrimination that can systematize inequality.
• Recent work has proposed optimal post-processing methods that randomize classification decisions for a fraction of individuals, in order to achieve fairness measures related to parity in errors and calibration.
• These methods, however, have raised concern due to the information inefficiency, intra-group unfairness, and Pareto sub-optimality they entail.