  The convergence of Artificial Intelligence (AI) and blockchain technology is
reshaping the digital world, offering decentralized, secure, and efficient AI
services on blockchain platforms. Despite the promise, the high computational
demands of AI on blockchain raise significant privacy and efficiency concerns.
The Optimistic Privacy-Preserving AI (opp/ai) framework is introduced as a
pioneering solution to these issues, striking a balance between privacy
protection and computational efficiency. The framework integrates
Zero-Knowledge Machine Learning (zkML) for privacy with Optimistic Machine
Learning (opML) for efficiency, creating a hybrid model tailored for blockchain
AI services. This study presents the opp/ai framework, delves into the privacy
features of zkML, and assesses the framework's performance and adaptability
across different scenarios.
