AudiFair: Privacy-Preserving Framework for Auditing Fairness

Abstract

Ensuring fairness in AI is challenging, especially when privacy concerns prevent access to proprietary models and training data. We propose a cryptographic framework for auditing fairness without requiring model disclosure. Unlike existing solutions – which either fail to capture attack vectors enabling dishonest model providers to pass audits unfairly, or require continuous auditing during deployment involving real-world users – our framework is the first to simultaneously achieve the following: (1) Model Privacy: Proprietary model details remain hidden from verifiers; (2) A dishonest model provider cannot cause a non-compliant model to pass the fairness audit without detection by the verifier; (3) Pre-deployment Certification: The certification can be conducted entirely prior to deployment, without requiring interaction with or data from real-world users; (4) Test Data Transparency: Test data for auditing is generated in a transparent and accountable way, preventing dishonest parties from manipulating it. We achieve these goals by carefully combining cryptographic commitments, coin tossing, and zero-knowledge proofs. We evaluate our protocol on decision trees, XGBoost, and neural networks. Our results confirm the practicality of our approach: for a dataset with 10,000 samples and 10 features, and an XGBoost model with 100 trees of depth 3, proof generation takes 42 seconds and verification takes 2 seconds.