Significant-decision ADMT without consumer access explanation

Store each ADMT decision's purpose, logic, output, and use at decision time, and return them in plain language from a consumer access-request path.

At decision time, write a decision-log row with the purpose, the key factors or reason codes, the model output, how the output was used, the human's role, and the model version. Provide an access-request path (privacy portal endpoint or DSAR export job) that verifies the requester and assembles a plain-language response from those rows using a template that explains the purpose, the logic, the outcome, and the person's other rights. Without decision-time records the access response cannot be reconstructed later, so the logging belongs in the decision service, not in the privacy team's tooling.

Where it goes: 2 data models, 9 AI output handling, 14 user-facing text.

What reviewers look for: decision rows carrying purpose, key factors, output, how it was used, and human role for every significant decision; an access-request handler or export job that reads those rows and returns a plain-language explanation plus rights information to the verified consumer.

score = model.predict_proba([features])[0][1]
applications.update(app_id, status='approved' if score >= 0.6 else 'denied')

score = model.predict_proba([features])[0][1]
status = 'approved' if score >= 0.6 else 'denied'
decision_log.insert({'consumer_id': cid, 'purpose': 'credit line eligibility',
                     'key_factors': reason_codes(features), 'model_output': score,
                     'how_used': 'scores of 0.6 or more approve; lower scores deny',
                     'human_role': 'underwriter reviews appeals', 'model_version': MODEL_VERSION})
applications.update(app_id, status=status)

Engineering guidance, not legal advice. Each provision below may add its own details (a cadence, a deadline, a required notice element): open it for those.