  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), enables
independent validation of AI-generated content without revealing sensitive
model information, promoting transparency and trust. ZKML enhances AI fairness
by providing cryptographic audit trails for model predictions and ensuring
uniform performance across users. We introduce snarkGPT, a practical ZKML
implementation for transformers, to empower users to verify output accuracy and
quality while preserving model privacy. We present a series of empirical
results studying snarkGPT's scalability and performance to assess the
feasibility and challenges of adopting a ZKML-powered approach to capture
quality and performance fairness problems in generative AI models.
