Artificial intelligence is deeply impacting our lives, from financial transactions to medical diagnoses, and even involving defense decision-making. However, as AI is widely applied across various industries, a key question arises: how do we ensure the reliability of these AI systems?

Recent data shows that the adoption rate of AI among enterprises has surged. In 2024, the proportion of companies using AI reached 78%, a significant increase compared to 55% a year ago. AI has permeated every major industry, but we still primarily rely on policies and commitments to trust the output results of AI. This trust mechanism is clearly insufficient in the current environment.

Considering the decision-making weight of AI in critical areas such as transaction auditing, health diagnosis recommendations, and military target recognition, we urgently need a technical standard to verify the accuracy and reliability of these decisions. In this context, Zero-Knowledge Proof (zkML) technology is emerging, aimed at redefining our trust mechanism in AI.

This technology aims to provide a verifiable method to ensure the accuracy of AI decisions while not disclosing sensitive information. This is particularly important in fields that require high security and privacy protection.

With the continuous development of AI technology and the expansion of its application scope, establishing a reliable verification standard has become increasingly important. This not only concerns the interests of individuals and businesses but also involves the entire society's confidence in AI technology.

In the future, we may see more solutions based on zero-knowledge proofs emerging to address the trustworthiness challenges of AI decision-making. This will provide the necessary trust foundation for the widespread application of AI, promoting the secure adoption of AI technology in more fields.