DTF / AI accountabilityDTF home

A model log records activity. DTF records the enterprise decision.

AI accountability breaks when evidence, policy, human authority, execution and outcomes are distributed across different systems. DTF binds them without becoming the model or recommendation engine.

01

The answer is only one part of the record.

A model can preserve its prompt, inputs, version and output while remaining silent about whether the output was accepted, which policy governed it, who could approve it and what action reached the real world.

DTF records that transition from model activity to enterprise action. This separates what the engine proposed from what an authorised person or policy allowed.

02

Accountability must survive change.

Models, policies, data sources and teams change. A later review must use the versions that governed the original decision, not whatever is current at the time of review.

Evidence

Decision-time context

Retain the evidence identity and relevant snapshot.

Governance

Pinned policy

Bind the policy version used at decision time.

People

Explicit authority

Record who could approve, reject or override.

Operations

Measured outcome

Connect the authorised action to the later result.

03

DTF is independent of the engine.

DTF does not recompute a customer recommendation or decide whether its answer is correct. It verifies the record around the decision and measures outcomes using declared, reproducible methods.

DTF / Next step

Bring one consequential decision.

Start with the evidence, policy, authority, action and outcome that should stay connected.

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