No human review/data-correction path after adverse ADMT decision

Give people who receive an adverse AI-influenced decision a way to see and correct the data used and to request human review that can change the outcome.

Built into the adverse-outcome path: when a decision is adverse, the system saves the personal-data inputs the model used with the decision, and the letter or screen that communicates it links to two routes. A data access and correction route (GET/PATCH /me/data, correct_input_data) shows those inputs and accepts corrections of inaccurate data, which send the decision back for re-run or review; a reconsideration route (POST /decisions/{id}/reconsideration, request_human_review) places the case in a review queue where a reviewer with authority to change the outcome records reviewer_id and override_reason.

Where it goes: 1 application source code, 9 AI output handling, 15 agent action surface, 2 data models.

What reviewers look for: for each adverse outcome that is saved and communicated, the inputs used stored with the decision, a person-facing route to view and correct them, and a reconsideration request that lands with a human reviewer who can change the outcome (reviewer_id, override_reason recorded); links to both routes in the adverse-outcome message.

if result['decision'] == 'denied':
    applications.save(app_id, status='denied')
    send_email(applicant.email, render('denial.txt', applicant=applicant))

if result['decision'] == 'denied':
    applications.save(app_id, status='denied', inputs_used=features, model_version=MODEL_VERSION)
    send_email(applicant.email, render('denial.txt', applicant=applicant,
               data_url=f'{BASE}/me/data', review_url=f'{BASE}/decisions/{app_id}/reconsideration'))

@app.patch('/me/data')
def correct_input_data(fix: DataCorrection, user=Depends(current_user)):
    corrections.create(user_id=user.id, field=fix.field, value=fix.value)
    review_queue.enqueue(user.latest_decision_id, reason='data_corrected')

@app.post('/decisions/{decision_id}/reconsideration')
def request_human_review(decision_id: str, user=Depends(current_user)):
    review_queue.enqueue(decision_id, reason='consumer_request')   # reviewer can change the outcome

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.