FairProof brings ZK proofs to certifying individual fairness of confidential ML models
chhaviyadav_ · x · 2026-11-05
The author, working with advisors including Dan Boneh, presents two research results:
- FairProof (ICML '24): the first ZKP framework for certifying individual fairness of confidential ML models, made practical by moving expensive computation offline and proving approximate fairness guarantees.
- ExpProof (ICML '25): cryptographically verifiable ML explanations — rather than forcing existing algorithms into expensive ZK circuits, the authors redesigned the underlying approach to fit proof systems.
Related event: FairProof: Verifying ML Fairness with Zero-Knowledge Proofs(2 posts)→
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