The privacy-explainability trade-off: unraveling the impacts of differential privacy and federated learning on attribution methods.

ABSTRACT: Since the advent of deep learning (DL), the field has witnessed a continuous stream of innovations. However, the translation of these advancements into practical applications has not kept pace, particularly in safety-critical domains where artificial intelligence (AI) must meet stringent regulatory and ethical standards. This is underscored by the ongoing research in eXplainable AI (XAI) and privacy-preserving machine learning (PPML), which seek to address some limitations associated with these opaque and data-intensive models. Despite brisk research activity in both fields, little attention has been paid to their interaction. This work is the first to thoroughly investigate the effects of privacy-preserving techniques on explanations generated by common XAI methods for DL models. A detail

SUBMITTER: Saifullah S 

PROVIDER: S-EPMC11253022 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature