We present a framework that allows to certify the fairness degree of a model based on an interactive and privacy-preserving test. The framework verifies any trained model, regardless of its training process and architecture. Thus, it allows us to evaluate any deep learning model on multiple fairness definitions empirically. We tackle two scenarios, where either the test data is privately available only to the tester or is publicly known in advance, even to the model creator. We investigate the soundness of the proposed approach using theoretical analysis and present statistical guarantees for the interactive test. Finally, we provide a cryptographic technique to automate fairness testing and certified inference with only black-box access to the model at hand while hiding the participants' sensitive data.
@article{arxiv.2009.01534,
title = {Fairness in the Eyes of the Data: Certifying Machine-Learning Models},
author = {Shahar Segal and Yossi Adi and Benny Pinkas and Carsten Baum and Chaya Ganesh and Joseph Keshet},
journal= {arXiv preprint arXiv:2009.01534},
year = {2021}
}