Can you guarantee a neural network treats similar people the same?

May 14, 2023

Testing a model for fairness can reveal problems, but it cannot promise their absence. For high-stakes uses, developers want a guarantee that a model treats similar individuals alike regardless of protected attributes such as race, sex, or age. Proving this is hard, because a neural network reaches its decisions through many non-linear computations.

Fairify, presented at ICSE 2023 by Sumon Biswas and Hridesh Rajan, verifies individual fairness in neural networks using an SMT solver. The key idea is that, for a given query, many neurons in the network always stay in the same state, which lets Fairify prune the network and make verification tractable enough for a developer to run. The approach is sound, so a result it certifies can be relied on.

Fairify moves fairness from something teams test for to something they can verify, which matters most where the cost of a biased decision is high. It is part of our lab’s work on the fairness and dependability of machine learning.

This work is part of Modular and Dependable AI. The full paper is available here.