AI systems receive Input Content, perform various forms of processing internally, and ultimately generate Output Content. However, examining only the Input and Output makes it difficult to determine what Decisions occurred between them.

From a Governance perspective, this creates a Black Box. Even when the final Output is preserved, the Decision Process cannot be reconstructed unless Evidence exists showing what was Summarized / Extracted, Compared / Analyzed, Evaluated / Assessed, and ultimately Recommended / Generated.

DSE preserves Evidence for each Governance-relevant Decision within this Process.

In the example shown, DSE #0024, #0025, #0026, and #0027 are generated across a sequence of AI Processes involving Summarize / Extract, Compare / Analyze, Evaluate / Assess, and Recommend / Generate. Each DSE preserves the state associated with the relevant Decision, including Fact, Evaluation, Policy, Authority, Process, Model / Context, and other relevant elements at the time of the Decision.

This makes it possible to trace an AI Process not merely as a transformation from Input to Output, but as a sequence of Governance-relevant Decisions.

Importantly, DSE does not attempt to explain every internal computation performed by an AI model. The Technical Black Box itself does not need to be completely eliminated. What Governance requires is not a complete explanation of internal computation, but the ability to reconstruct Governance-relevant Decisions and the states in which those Decisions were formed as Evidence.

Introducing DSE also does not necessarily change the Input or Output itself.

Same input. Same output. What changes is the evidence in between.

Even when the same Output is generated from the same Input, the presence of Evidence makes it possible to verify which Decisions led to that Output.

DSE therefore does not primarily change the AI Output itself. It creates verifiable Evidence for Decision Processes that would otherwise pass through the system as a Black Box.