Adverse AI decisions deployed without appeal/reversal-rate monitoring

Link appeal outcomes to each AI decision record, track the reversal rate per model version, and alert and suspend the model when it crosses a set threshold.

When an appeal or review closes, the appeal handler writes the outcome (upheld or overturned) back onto the original decision record, which carries the model and version that produced it. A metric job or counter computes the reversal rate per model version, and an alert rule fires when it crosses a threshold chosen in advance; because few people appeal, each appeal is read as a sample of the error rate across all decisions, not an isolated event. The alert links to a runbook, and a feature flag lets on-call staff suspend the model (routing new cases to manual review) or send it for retraining.

Where it goes: 2 data models, 10 logs and telemetry, 3 config and feature flags.

What reviewers look for: appeal_outcome or overturned fields joined to decision records with model_version; a reversal_rate metric broken down by model version; an alert rule on that rate; and a suspension runbook or flag the alert points to, rather than appeals handled case by case with nothing feeding back to model monitoring.

def close_appeal(appeal, outcome):
    appeal.status = 'closed'
    appeal.outcome = outcome
    db.commit()

APPEALS = Counter('ai_appeal_outcomes_total', 'Closed appeals of AI decisions',
                  ['model_version', 'outcome'])

def close_appeal(appeal, outcome):          # outcome: 'upheld' | 'overturned'
    appeal.status = 'closed'
    appeal.outcome = outcome
    decision = db.get(Decision, appeal.decision_id)
    decision.appeal_outcome = outcome       # joined to the decision record
    db.commit()
    APPEALS.labels(model_version=decision.model_version, outcome=outcome).inc()

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.