Risk assessment and mitigation in ML projects
Instruction and application
CompleteEvery ML project has uncertainty - but not all uncertainty is unmanaged risk. ML systems learn from data that changes, serve probabilistic predictions and often affect people directly. This section helps you recognise ML-specific risks, assess them and plan mitigations and ethical compliance early.
Recognising and assessing ML-specific risks
| Category | Example risks |
|---|---|
| Data | Incomplete or biased training data, poor quality, late availability |
| Model | Overfitting, underperformance, drift, poor generalisation |
| Operational | Integration failures, scale limits, deployment errors |
| Stakeholder | Misaligned expectations, low trust, unclear ownership |
| Ethical / regulatory | Biased outputs, poor explainability, non-compliance |
How to assess ML risks
- Risk matrix: Score likelihood and impact; prioritise high-high items (e.g. fairness in regulated use).
- Stakeholder conversations: Legal, risk, engineering and product surface cross-cutting issues early.
- Document assumptions: Data coverage, user behaviour, performance in subgroups - unchallenged assumptions often become incidents.
Example
A ride-sharing and insurer pilot uses mostly pre-pandemic data. Post-pandemic behaviour shifts cause demand errors; missing demographics in two regions surface drift anddata completeness risks. Without monitoring and validation protocols, services delay and trust erodes.
Mitigating risks and ethical compliance
Mitigations
- Fallbacks: Rules or human review for high-stakes decisions.
- Scheduled retraining: Cadence or drift-triggered refresh.
- Pilots and A/B tests: Limited rollout before full scale.
- Monitoring and alerting: Accuracy, fairness metrics, latency, drift.Example: A telecom provider routes to a rule-based backup when complaint-prediction accuracy drops below 75%.
Ethical and compliance focus areas
- Bias audits: Compare error rates and outcomes across groups; plan mitigations when disparities appear.
- Explainability: SHAP, LIME or simpler attribution - match depth to audience (regulators vs engineers).
- Regulatory alignment: GDPR, HIPAA, sector rules - involve compliance early.
- Consent and usage: Confirm training use rights; anonymise or pseudonymise where required.
Action item: Spot the risks before they happen
Scenario: You launch a model that predicts which customers may need support in the next 30 days, using location, product usage and past tickets. Record your thinking in the form below.