JSTprove: Pioneering Verifiable AI for a Trustless Future

TL;DR

JSTprove introduces a user-friendly zkML toolkit that enables AI developers to generate verifiable proofs of AI inferences, enhancing trust, security, and transparency in critical applications without requiring cryptographic expertise.

Contribution

The paper presents JSTprove, a practical zkML toolkit built on Polyhedra Network's backend, simplifying verifiable AI inference for ML engineers and fostering future research and deployment.

Findings

JSTprove enables easy generation and verification of AI inference proofs.

The toolkit improves transparency and trust in AI decision-making.

Real-world use cases demonstrate its practical applicability.

Abstract

The integration of machine learning (ML) systems into critical industries such as healthcare, finance, and cybersecurity has transformed decision-making processes, but it also brings new challenges around trust, security, and accountability. As AI systems become more ubiquitous, ensuring the transparency and correctness of AI-driven decisions is crucial, especially when they have direct consequences on privacy, security, or fairness. Verifiable AI, powered by Zero-Knowledge Machine Learning (zkML), offers a robust solution to these challenges. zkML enables the verification of AI model inferences without exposing sensitive data, providing an essential layer of trust and privacy. However, traditional zkML systems typically require deep cryptographic expertise, placing them beyond the reach of most ML engineers. In this paper, we introduce JSTprove, a specialized zkML toolkit, built on Polyhedra Network's Expander backend, to enable AI developers and ML engineers to generate and verify proofs of AI inference. JSTprove provides an end-to-end verifiable AI inference pipeline that hides cryptographic complexity behind a simple command-line interface while exposing auditable artifacts for reproducibility. We present the design, innovations, and real-world use cases of JSTprove as well as our blueprints and tooling to encourage community review and extension. JSTprove therefore serves both as a usable zkML product for current engineering needs and as a reproducible foundation for future research and production deployments of verifiable AI.