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We present a framework that allows to certify the fairness degree of a modelbased on an interactive and privacy-preserving test. The framework verifies anytrained model, regardless of its training process and architecture. Thus, itallows us to evaluate any deep learning model on multiple fairness definitionsempirically. We tackle two scenarios, where either the test data is privatelyavailable only to the tester or is publicly known in advance, even to the modelcreator. We investigate the soundness of the proposed approach usingtheoretical analysis and present statistical guarantees for the interactivetest. Finally, we provide a cryptographic technique to automate fairnesstesting and certified inference with only black-box access to the model at handwhile hiding the participants' sensitive data.

Fairness in the Eyes of the Data: Certifying Machine-Learning Models