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FairProof : Confidential and Certifiable Fairness for Neural Networks
Feb. 21, 2024, 5:41 a.m. | Chhavi Yadav, Amrita Roy Chowdhury, Dan Boneh, Kamalika Chaudhuri
cs.LG updates on arXiv.org arxiv.org
Abstract: Machine learning models are increasingly used in societal applications, yet legal and privacy concerns demand that they very often be kept confidential. Consequently, there is a growing distrust about the fairness properties of these models in the minds of consumers, who are often at the receiving end of model predictions. To this end, we propose FairProof - a system that uses Zero-Knowledge Proofs (a cryptographic primitive) to publicly verify the fairness of a model, while …
abstract applications arxiv concerns consumers cs.ai cs.cr cs.lg demand fairness legal machine machine learning machine learning models networks neural networks privacy type
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