Trustworthy AI

Provenance

For Provenance, we define applicability from the purpose and constraints, including conditions where it should not be used. Avoid locking the design to one model or platform, separate data, integration, and monitoring, and record the reasons for each choice.

Scope

For Provenance, we define applicability from the purpose and constraints, including conditions where it should not be used.

Design decisions

For Provenance, we avoid locking the design to one model or platform, separate data, integration, and monitoring, and record the reasons for each choice.

Validation

For Provenance, we use repeatable inputs and criteria and inspect failure cases as well as expected behavior.

Operational review

For Provenance, we track changes, quality shifts, and use patterns while keeping a path for rollback.

Human governance

For Provenance, we do not leave consequential decisions to automation alone and define accountable review points.