Trust the Process: Zero-Knowledge Machine Learning to Enhance Trust in Generative AI Interactions

Generative AI, exemplified by models like transformers, has opened up new possibilities in various domains but also raised concerns about fairness, transparency and reliability, especially in fields like medicine and law. This paper emphasizes the urgency of ensuring fairness and quality in these domains through generative AI. It explores using cryptographic techniques, particularly Zero-Knowledge Proofs (ZKPs), to address concerns regarding performance fairness and accuracy while protecting model privacy. Applying ZKPs to Machine Learning models, known as ZKML (Zero-Knowledge Machine Learning), …

ai interactions concerns cs.cr cs.lg domains fairness fields generative interactions knowledge law machine machine learning medicine paper process quality reliability through transformers transparency trust trust in generative ai