Model documentation and approval, fairness and bias testing, explainability where it is required, audit trails, and regulated-industry model risk.
3 questions in MLOps & ML Platform.
A statement of intended and out-of-scope use, the provenance and lawful basis of the training data, evaluation results broken down by segment rather than only in aggregate, the known limitations and failure modes, and a signed approval record naming who accepted the risk against which evidence.
Establish who needs the explanation and what they will do with it, because a global account of model behaviour and a per-decision reason serve different needs. Attribution methods give you defensible local reasons within limits, but a post-hoc explanation is a second model of the first, not its reasoning.
Decide which groups you are testing across and how you will obtain that attribute, choose one fairness definition and justify it - because the common definitions are mathematically incompatible and cannot all be satisfied - then report performance and error rates per group with sample sizes and intervals attached.