Dikaios: Privacy Auditing of Algorithmic Fairness via Attribute Inference Attacks. (arXiv:2202.02242v2 [cs.CR] UPDATED)

Machine learning (ML) models have been deployed for high-stakes applications.
Due to class imbalance in the sensitive attribute observed in the datasets, ML
models are unfair on minority subgroups identified by a sensitive attribute,
such as race and sex. In-processing fairness algorithms ensure model
predictions are independent of sensitive attribute. Furthermore, ML models are
vulnerable to attribute inference attacks where an adversary can identify the
values of sensitive attribute by exploiting their distinguishable model
predictions. Despite privacy and fairness being …

attacks auditing fairness privacy