Fair Classification with Adversarial Perturbations
This work addresses fairness in classification for scenarios where protected attributes may be adversarially manipulated, which is an incremental advance over prior methods that rely on stochastic assumptions.
The paper tackles fair classification when an adversary can arbitrarily perturb protected attributes in a fraction of training samples, addressing issues like strategic misreporting or errors, and presents an optimization framework with provable guarantees on accuracy and fairness, showing near-tightness of these guarantees and empirical evaluation on real-world datasets.
We study fair classification in the presence of an omniscient adversary that, given an $η$, is allowed to choose an arbitrary $η$-fraction of the training samples and arbitrarily perturb their protected attributes. The motivation comes from settings in which protected attributes can be incorrect due to strategic misreporting, malicious actors, or errors in imputation; and prior approaches that make stochastic or independence assumptions on errors may not satisfy their guarantees in this adversarial setting. Our main contribution is an optimization framework to learn fair classifiers in this adversarial setting that comes with provable guarantees on accuracy and fairness. Our framework works with multiple and non-binary protected attributes, is designed for the large class of linear-fractional fairness metrics, and can also handle perturbations besides protected attributes. We prove near-tightness of our framework's guarantees for natural hypothesis classes: no algorithm can have significantly better accuracy and any algorithm with better fairness must have lower accuracy. Empirically, we evaluate the classifiers produced by our framework for statistical rate on real-world and synthetic datasets for a family of adversaries.