Framework overview
- Objective: quantify operational, revenue and regulatory risk from autonomous ML decisioning so the board can set acceptable tolerance and trigger governance actions.
- Approach: translate technical failure modes → business KPIs → risk scores → thresholds → prescriptive actions.
Step 1 — classify failure modes
- Accuracy drift (label distribution shift, concept drift)
- Calibration bias (systematic over/under scoring by cohort)
- Availability/outage
- Adversarial/manipulation risk
- Optimization externalities (gaming, price cascades)
Step 2 — map to business KPIs & metrics
- Revenue impact: expected loss/gain per decision (E[ΔRevenue]) and volatility (std dev)
- Compliance risk: % decisions violating rules, regulatory fines exposure (expected fine per period)
- Customer experience: churn delta, NPS impact
- Operational risk: MTTR, % decisions auto-handled vs. escalated
- Reputation: social sentiment delta, media-incident score
Step 3 — quantify exposure
- Build scenario trees: for each failure mode simulate probability p and conditional business impact I → expected risk = p * I.
- Use stress tests (worst-case percentiles), backtesting, counterfactual A/B to estimate I.
- Aggregate using weighted sum and convert to normalized risk score (0–100) per domain.
Step 4 — thresholds & governance triggers
- Green (0–40): monitor; continuous evaluation and monthly reporting
- Amber (41–70): require executive sign-off, conservative rollout (reduced autonomy), weekly mitigation plan (retraining, recalibration, throttling)
- Red (71–100): immediate rollback/disablement, regulatory disclosure, incident response, board review
Step 5 — controls, telemetry & reporting
- Real-time dashboards: KPI deltas, cohort calibration, drift detectors, anomaly alerts
- Audit trail: decision logs, model version, data snapshot, reason codes
- KPIs in board pack: top-line expected loss, compliance breaches (30/90-day), uptime SLA, mitigation status
- Review cadence: daily ops for red/amber, monthly for green; quarterly model risk review with independent validation
Implementation notes & trade-offs
- Prioritize high-impact failure modes first; combine statistical estimates with expert judgement.
- Use financial proxies for non-monetary harms (reputation weight) and revisit weights periodically.
- Ensure explainability and human-in-the-loop thresholds for sensitive segments to reduce regulatory exposure.
This framework provides transparent, quantifiable risk exposure and concrete governance triggers so the board can set acceptable tolerance levels tied to business impact.
