Auditing Fairness-Privacy Trade-offs: Subpopulation-Level Effects of Fairness-Enhancing Algorithms
A new preprint presents the first comprehensive study of how fairness-enhancing algorithms impact membership inference privacy risks at the subpopulation level. The authors adapt the Likelihood Ratio Attack for subgroup auditing, revealing privacy disparities that aggregate evaluations can obscure. They also show that the benefits and costs of differential privacy are unevenly distributed across subpopulations, and introduce a unified empirical framework for jointly evaluating fairness, privacy, and utility.
Why it matters: This work demonstrates that fairness interventions can have uneven privacy impacts across subpopulations, underscoring the need for subgroup-level auditing to avoid hidden disparities.
Full story at: arXiv Machine Learning ↗