Accountable AI with ZKPs: Certifying Fairness and Explanations under model Confidentiality

Responsible deployment of AI models in high-stakes societal applications requires that these models be trustworthy—exhibiting properties such as fairness, privacy and interpretability. However, legal and IP constraints often necessitate that models remain confidential, which leads to the breakdown of many trustworthy AI tools in practice. This tension gives rise to a central challenge: how can we prove and verify key properties of ML models without revealing the models themselves? In this talk, I will present my recent work that addresses this challenge using zero-knowledge proofs (ZKPs). Specifically, I will describe: (1) FairProof, a system for publicly certifying individual fairness in neural networks while preserving model confidentiality, and (2) ExpProof, which operationalizes explanations even in adversarial settings. Together, these systems advance the goal of building verifiable and accountable AI.