CertiFair: A Framework for Certified Global Fairness of Neural Networks

TL;DR

CertiFair introduces a framework for verifying and training neural networks to ensure global individual fairness, combining a sound verifier with a fairness-aware training method that significantly improves fairness metrics.

Contribution

The paper presents a novel verifier for checking fairness in neural networks and a training approach that enforces fairness with provable guarantees, addressing both verification and mitigation.

Findings

Verifier accurately detects fairness violations in ReLU neural networks.

Fairness training improves global individual fairness by up to 96%.

Minimal impact on test accuracy during fairness enforcement.

Abstract

We consider the problem of whether a Neural Network (NN) model satisfies global individual fairness. Individual Fairness 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 provide 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 significant drop in test accuracy.