We consider the problem of whether a Neural Network (NN) model satisfies global individual fairness. Individual Fairness (defined in (Dwork et al. 2012)) suggests that similar individuals with respect to a certain task are to be treated similarly by the decision model. In this work, we have two main objectives. The first is to construct a verifier which checks whether the fairness property holds for a given NN in a classification task or provides a counterexample if it is violated, i.e., the model is fair if all similar individuals are classified the same, and unfair if a pair of similar individuals are classified differently. To that end, we construct a sound and complete verifier that verifies global individual fairness properties of ReLU NN classifiers using distance-based similarity metrics. The second objective of this paper is to provide a method for training provably fair NN classifiers from unfair (biased) data. We propose a fairness loss that can be used during training to enforce fair outcomes for similar individuals. We then provide provable bounds on the fairness of the resulting NN. We run experiments on commonly used fairness datasets that are publicly available and we show that global individual fairness can be improved by 96 % without a significant drop in test accuracy.
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This content will become publicly available on July 22, 2025
FairProof: CONFIDENTIAL AND CERTIFIABLE FAIRNESS FOR NEURAL NETWORKS
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.
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- Award ID(s):
- 2217058
- PAR ID:
- 10517272
- Publisher / Repository:
- Journal of Machine Learning Research
- Date Published:
- Journal Name:
- Proceedings of Machine Learning Research
- ISSN:
- 2640-3498
- Format(s):
- Medium: X
- Location:
- Vienna
- Sponsoring Org:
- National Science Foundation
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