As AI systems increasingly integrate into critical sectors, the need for transparency and provability in their decisions has become crucial. Currently, many AI models operate as black boxes, making it difficult to understand how decisions are made, which is particularly concerning in high-stakes fields like healthcare and finance. There is a growing consensus that technical transparency measures are mandatory, emphasizing the importance of audit trails for AI decisions. Zero-knowledge proofs (ZKPs) present a viable solution, allowing verification of AI computations without exposing sensitive data. This technology makes it possible to ensure AI systems operate transparently and ethically, thereby fostering trust in their deployment. The goal is to transition from mere promises of safety to verifiable guarantees, especially as we approach the development of Artificial Superintelligence (ASI). Establishing effective and scalable ZKPs is essential for maintaining control over evolving AI capabilities and ensuring alignment with human values.

Source đź”—