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.

翻译：机器学习模型日益广泛应用于社会应用场景，但法律与隐私要求往往需对其严格保密。因此，消费者（通常是模型预测的接收方）对这些模型的公平属性日益产生不信任。为此，我们提出FairProof系统——该系统利用零知识证明（一种密码学原语）在保持模型机密性的同时公开验证其公平性。我们还提出了一种适用于全连接神经网络的公平性认证算法，该算法与零知识证明特性相适配并应用于本系统。我们在Gnark中实现了FairProof，并通过实验证明了该系统的实际可行性。