Skip to main content

Skills application

Skills application
Complete

Responsible AI development (healthcare scenario)​

You are an AI ethics and governance consultant reviewing data practices atHealth Insights AI, a startup building a cardiovascular risk model from EHRs, wearables, genetics and patient-reported symptoms.

Skills application illustration

Context​

Health Insights AI scenario

Stakeholders worry about sensitive health data,bias in diagnostics and whether practices meet UK GDPR and broader clinical-data expectations (use HIPAA only where it truly applies to your scenario).

Success criteria​

✨

Bias and error analysis:

≥3 risks from sources, retention or metadata gaps; fairness and accuracy impact.

✨

Security and compliance:

≥2 legal/ethical requirements; what to audit in current ML security posture.

✨

Security protocol design:

≥2 protocols (for example differential privacy, federated patterns, strengthened de-identification with risk assessment).

✨

Bias minimisation:

≥3 collection/preprocessing strategies grounded in the scenario.

Completing this activity unlocks the solution example on the following page.

Scenario documentation​

Data sources and lineage​

✨

EHRs:

rich clinical and demographic history; long retention; logging granularity varies by contributing hospital.

✨

Wearables:

continuous streams aggregated daily; raw one year, aggregates five years; device-specific algorithms not harmonised.

✨

Genetics:

separate secure store; retention like EHRs;uneven ethnic representation from historical study recruitment.

✨

App symptoms:

free text + structured forms; three-year retention; NLP extractionpoorly documented.

Retention (summary)​

  • EHR/genetics: long-term clinical retention patterns.
  • Wearables: raw 1y / aggregates 5y.
  • App: 3y.
  • Metadata: partial;no central catalogue; transforms and bias context inconsistently recorded.

Security (ML-relevant)​

  • Mixed de-identification; mapping controls exist but effectiveness not fully documented.
  • Encryption in transit and at rest; audits skew to infra, not ML-specific re-identification risk.
  • Access policies exist; fairness expectations for ML still immature.

Preprocessing​

  • Heterogeneous EHR coding; wearable metric differences not tested for bias; genetic missingness; NLP bias unevaluated; simple imputation without slice-aware analysis; expert-driven feature picks without proxy review.

Tasks​

  1. Research: UK GDPR + health-data themes; common clinical ML bias sources; lineage/retention roles.
  2. Bias and error analysis
  3. Security and compliance evaluation
  4. Security protocol design
  5. Bias minimisation
  6. Synthesise a concise report for Health Insights AI.

Action item: Submit your report​

Draft in the form below; export for your governance forum if needed.

Introduction and problem statement
0 words
Bias and error source analysis
0 words
Data security and compliance evaluation
0 words
Data security protocol design
0 words
Bias minimisation in data handling
0 words
Conclusion
0 words
In progress