Responsible AI and Governance

Responsible AI governance turns ML risk into explicit ownership, documentation, approvals, monitoring, and incident response. It should be practical enough to use during releases, not only as final paperwork.

Governance Artifacts

Artifact Contents
Model card Intended use, metrics, limitations, risks, owners, release status.
Dataset card Source, license, consent, preprocessing, quality, privacy, exclusions.
Risk assessment Impact, likelihood, affected users, controls, residual risk.
Eval report Cases, slices, thresholds, failures, approvals.
Red-team report Attack classes, severity, mitigations, open issues.
Incident record Impact, detection, root cause, corrective actions.

Risk Classification

Risk Control
Low Standard evals and monitoring.
Medium Owner approval, slice evals, rollback plan.
High Human oversight, safety review, red team, incident playbook.
Regulated Legal/compliance review, documentation, audit trail, retention policy.

Human Oversight

Human oversight needs defined authority. A human rubber-stamping model output is not meaningful oversight. Define what humans can review, override, appeal, and escalate.

Practical Lab: Release Approval Checklist

release:
  model_card_updated:
  dataset_card_updated:
  eval_report_attached:
  red_team_complete:
  fairness_slices_reviewed:
  monitoring_ready:
  rollback_tested:
  approver:

Study Cards

Question

What should a model card support?

Answer

Release decisions by documenting intended use, metrics, limitations, risks, and operational constraints.

Question

Why classify ML risk?

Answer

Risk classification determines the required evals, approvals, monitoring, and oversight.

Question

What makes human oversight meaningful?

Answer

Humans must have authority, context, and a defined ability to review, override, appeal, or escalate.

References