AI Advancements Spark Urgent Call for Transparency

Artificial intelligence (AI) is increasingly integrated into critical sectors of society, such as healthcare, finance, and law enforcement. These systems are no longer limited to recommending entertainment or products; they now diagnose illnesses, approve loans, detect fraud, and identify threats. As AI becomes more embedded in our daily lives, it is crucial to ensure that its outputs are provable and that it acts in our best interest.

Most AI systems operate as black boxes, making it difficult to understand how they arrive at decisions or whether they are acting as intended. This lack of transparency poses serious risks, especially in high-stakes sectors. AI models may unknowingly encode bias, manipulate outcomes, or behave in ways that conflict with legal or ethical norms. Without a verifiable trail, users are left guessing whether a decision was fair, valid, or even safe.

The concerns about AI's opacity become more pressing when considering the rapid advancements in AI capabilities. There is a broad consensus that developing an Artificial Superintelligence (ASI) is inevitable. An ASI would surpass human intelligence across all domains, from scientific reasoning to strategic planning, creativity, and emotional intelligence. If a superintelligent system acts in ways humans can’t predict or understand, ensuring it aligns with our values becomes a critical challenge.

Scenarios where an ASI could threaten humanity are apparent even to AI advocates. Experts warn of AI systems capable of civilization-level cyberattacks, mass manipulation, and autonomous warfare. With so many possible scenarios, ensuring that an ASI doesn’t pose an existential threat to humanity is paramount.

The solution to these challenges lies in verifiability. Relying on promises from opaque models is no longer acceptable for their integration into critical infrastructure. We need guarantees and proof. There is a growing consensus in policy and research communities that technical transparency measures are needed for AI. Regulatory discussions often mention audit trails for AI decisions, highlighting the importance of AI systems being “traceable” and “understandable.”

Fortunately, advancements in other fields, such as advanced cryptography, can be applied to AI to ensure that systems remain in check and aligned with human interests. Zero-knowledge proofs (ZKPs) offer a novel way to achieve traceability that is immediately applicable to AI systems. ZKPs can embed this traceability into AI models from the ground up, generating an immutable proof of what happened without exposing internal model weights or sensitive data.

Using zkML libraries, we can combine zero-knowledge proofs and machine learning to verify all the computations produced on these models. This effectively takes AI out of a black box and lets us know exactly where it stands and how it got there. More importantly, it keeps humans in the loop. AI development needs to be open, decentralized, and verifiable, and zkML needs to achieve this.

This needs to happen today to maintain control over AI tomorrow. We need to make sure that human interests are protected from day one by being able to guarantee that AI is operating as we expect it to before it becomes autonomous. In the short term, zkML is about ensuring that we can trust AI with the automation of sensitive processes like loans, diagnoses, and policing because we have proof that it operates transparently and equitably.

As AI systems become more capable and integrated into critical domains, proving what they do—and how they do it—will be essential. Verifiability must move from a research concept to a design principle. With tools like zkML, we now have a viable path to embed transparency, security, and accountability into the foundations of AI. The question is no longer whether we can prove what AI does, but whether we choose to.