# FairProof : Confidential and Certifiable Fairness for Neural Networks

**Authors:** Chhavi Yadav, Amrita Roy Chowdhury, Dan Boneh, Kamalika Chaudhuri  
**arXiv:** [2402.12572](https://arxiv.org/abs/2402.12572)  
**Date:** 2024-02-19  
**Summary page:** https://www.summarizepaper.com/fr/paper/2402.12572v1/

## 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 maintaining confidentiality. We also propose a fairness certification algorithm for fully-connected neural networks which is befitting to ZKPs and is used in this system. We implement FairProof in Gnark and demonstrate empirically that our system is practically feasible.