Differentially-private and plausible counterfactuals

TMLR Paper6484 Authors

Abstract: Counterfactual explanations are particularly appealing in high-stakes domains such as finance and hiring, as they provide affected users with suggestions on how to alter their profiles to receive a favorable outcome. However, existing methods are characterized by a privacy-quality trade-off. More precisely, as highlighted in recent works, instance-based approaches generate plausible counterfactuals but are vulnerable to privacy attacks, while perturbation-based methods offer better privacy at the cost of lower explanation quality. In this paper, we propose to solve this dilemma by introducing a diverse set of differentially-private mechanisms for generating counterfactuals, providing a high resistance against privacy attacks while maintaining high utility. These mechanisms can be integrated at different stages of the counterfactual generation pipeline i.e, pre-processing, in-processing or post-processing), thereby offering maximal flexibility during the design for the model provider. We have performed an empirical evaluation of the proposed approaches on a wide range of datasets and models to evaluate their effect on the privacy and utility of the generated counterfactuals. Overall, the results obtained demonstrate that in-processing methods significantly reduce the success rate of privacy attacks while moderately impacting the quality of counterfactuals generated. In contrast, pre-processing and post-processing mechanisms achieve a higher level of privacy but at a greater cost in terms of utility, thus being more suitable for scenarios in which privacy is paramount.

Submission Type: Long submission (more than 12 pages of main content)

Assigned Action Editor: ~Sanghamitra_Dutta2

Submission Number: 6484