Enforcing Group Fairness in Algorithmic Decision Making: Utility Maximization Under Sufficiency
This work addresses fairness in algorithmic decision-making for applications like hiring or lending, but it is incremental as it builds on existing optimization frameworks and fairness criteria.
The paper tackles the problem of enforcing group fairness in algorithmic decision-making by formulating it as a constrained optimization problem, focusing on fairness concepts like PPV parity, FOR parity, and sufficiency, and finds that optimal rules can lead to counter-intuitive outcomes such as selecting individuals with the smallest utility for one group or causing within-group unfairness.
Binary decision making classifiers are not fair by default. Fairness requirements are an additional element to the decision making rationale, which is typically driven by maximizing some utility function. In that sense, algorithmic fairness can be formulated as a constrained optimization problem. This paper contributes to the discussion on how to implement fairness, focusing on the fairness concepts of positive predictive value (PPV) parity, false omission rate (FOR) parity, and sufficiency (which combines the former two). We show that group-specific threshold rules are optimal for PPV parity and FOR parity, similar to well-known results for other group fairness criteria. However, depending on the underlying population distributions and the utility function, we find that sometimes an upper-bound threshold rule for one group is optimal: utility maximization under PPV parity (or FOR parity) might thus lead to selecting the individuals with the smallest utility for one group, instead of selecting the most promising individuals. This result is counter-intuitive and in contrast to the analogous solutions for statistical parity and equality of opportunity. We also provide a solution for the optimal decision rules satisfying the fairness constraint sufficiency. We show that more complex decision rules are required and that this leads to within-group unfairness for all but one of the groups. We illustrate our findings based on simulated and real data.