Decision Traceability
Every output can be traced back through the reasoning, context, and data that produced it.
Decision Traceability is Reasoning Visibility extended to the full chain. It's not enough to see the reasoning for the current output — you need to trace it back to the inputs that shaped that reasoning. Which context was the model given? What data did it read? What prior decisions influenced this one? Decision Traceability creates an unbroken chain from input to output, making it possible to diagnose errors, verify correctness, and reproduce results. It's the engineering discipline behind transparent AI.
More in System Transparency
System Opacity
The inability to see what an AI system knows, why it made a decision, or what it did. The default state of most AI tools today.
Decision Fog
The user can see the output but not the reasoning, trade-offs, or alternatives the model considered.
Black Box Agency
An agent that acts on your behalf but won't show its work. You get results with no audit trail.
Invisible State
The system has internal state that affects behavior but is not exposed to the user.
Accountability Gap
No one — human or system — can explain why a specific output was produced.
Reasoning Visibility
The user can inspect the model's chain of thought, assumptions, and decision points.
Open State
The system's internal state is visible and inspectable at any time. The opposite of Invisible State.
Audit Trail
A complete, immutable record of what the system did, when, why, and with what inputs.