Explainability is important in AI governance, but it is not sufficient to govern AI systems in practice.

When an AI system makes a decision or produces an output, understanding why it reached that result can help identify problems and improve models or operational practices. Explainability is particularly important for post-incident analysis, audits, and examinations of responsibility following an AI Incident.

However, being able to explain a decision is not the same as being able to govern it.

The first limitation is Does not scale. Modern AI systems can generate enormous numbers of decisions and outputs in very short periods. It is not realistic for humans to read an explanation and assess its validity before every subsequent action. “Explanations do not scale to the speed and volume of modern AI systems” captures this problem directly.

Second, Explainability Cannot enforce policy. Even if an AI system can explain why it made a decision, the explanation itself does not stop execution when that decision violates an organizational Policy or legal requirement.

Third, Explainability Cannot assign responsibility. Understanding how an AI system reached a decision does not automatically establish who had authority over that decision, who approved it, or which institutions and Policies applied. These are governance questions distinct from technical explanation.

Most importantly, Explainability Cannot prevent unsafe actions. Even a complete explanation of a dangerous decision does not provide safety if the harmful action has already been executed by the time the explanation becomes available. Explainability can help us understand why something happened, but it does not by itself provide a mechanism to prevent it from happening.

I2E therefore does not reject Explainability, but treats it as one component of governance. What is required is not explanation alone, but identification of the applicable Policy, Authority, and Context, preservation of a verifiable state as Decision-State Evidence, and reliable application of governance requirements at the execution stage through the Execution Boundary.

“Explanation helps us understand a decision. Governance determines whether that decision may be executed.”

This distinction marks an important boundary between Explainability and Governance.