Impact on bias mitigation algorithms to variations in inferred sensitive attribute uncertainty.

ABSTRACT: Concerns about the trustworthiness, fairness, and privacy of AI systems are growing, and strategies for mitigating these concerns are still in their infancy. One approach to improve trustworthiness and fairness in AI systems is to use bias mitigation algorithms. However, most bias mitigation algorithms require data sets that contain sensitive attribute values to assess the fairness of the algorithm. A growing number of real world data sets do not make sensitive attribute information readily available to researchers. One solution is to infer the missing sensitive attribute information and apply an existing bias mitigation algorithm using this inferred knowledge. While researchers are beginning to explore this question, it is still unclear how robust existing bias mitigation algorithms are t

SUBMITTER: Wang Y 

PROVIDER: S-EPMC11924408 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature