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Skills application

Skills application
Complete

Data management for ethical ML models​

You are a data ethics and governance analyst reviewing practices at a company building ML models. Analyse lineage, retention, metadata, security, collection and preprocessing, then recommend improvements for fair, accountable development.

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Context​

Insight Innovations scenario

Insight Innovations Ltd builds AI solutions across industries. It is training acustomer churn model using transaction history, website activity logs, demographics and customer-service interactions to target retention offers.

Internal concerns include bias,opaque feature pipelines,retention and whether practices align with regulation and ethics. Your deliverable is anethical data management assessment with actionable recommendations.

Success criteria​

  • Bias and error analysis: at least three risks from lineage, retention and metadata gaps; impact on fairness and accuracy.
  • Security and compliance evaluation: at least two relevant regulations or principles (for example UK GDPR, PECR where relevant); what to audit in current security posture for ML fairness and transparency.
  • Security protocol design: at least two concrete protocols balancing protection with fair training (for example anonymisation, pseudonymisation, differential privacy, access tiers).
  • Bias minimisation in data handling: evaluate collection and preprocessing risks; propose at least three mitigation strategies.

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

Additional documentation (scenario facts)​

1. Data sources and lineage​

  • Transaction history: purchases, dates, amounts, payment methods; retainedlife of customer + 5 years; basic timestamps;limited transformation tracking.
  • Website logs: pages, dwell time, cart actions;aggregated daily, kept3 years; raw logs deleted after3 months; aggregation may drop granular actions.
  • Demographics: registration and surveys; fields vary historically; retention matches transaction DB.
  • Service interactions: CRM transcripts and summaries;2 years retention; sentiment scores fed into churn model;sentiment model training poorly documented.

2. Retention (summary)​

  • Transaction + demographics: life + 5 years.
  • Web logs: 3 years aggregated; raw 3 months.
  • Service: 2 years.
  • Metadata: limited automation; schemas partly documented;no central metadata repository; weak records on quality, transforms and bias context.

3. Metadata management​

  • Schemas drift from production.
  • Partial data dictionaries; little on quality, bias context or collection rationale.
  • No formal transform rationale log; provenance for fields often missing.

4. Security (ML-relevant)​

  • Pseudonymisation for analytics; ID mapping in restricted store.
  • Small group with raw DB access; TLS in transit; AES-256 at rest.
  • Audits exist but ML-specific fairness/security scope not explicit.
  • Fairness guidelines for ML still immature.

5. Collection and preprocessing​

  • Direct extracts from core DBs; daily web aggregation; sentiment from in-house tool (weak documentation).
  • Missing value handling: mean/mode imputation; group-wise impact not studied.
  • Feature selection by domain intuition and early accuracy; no systematic bias amplification review.

Task steps​

  1. Research: brief notes on ethical data management for ML, UK GDPR and PECR themes, common bias sources; tie to lineage, retention and metadata.
  2. Bias and error analysis (report section).
  3. Security and compliance evaluation (report section).
  4. Security protocol design (report section).
  5. Bias minimisation (report section).
  6. Synthesis: concise report with recommendations for Insight Innovations Ltd.

Go deeper (after your draft)​

  • How does strong metadata improve auditability of bias?
  • What are trade-offs between strict security controls and data volume for fair training?
  • What ongoing monitoring should govern lineage, quality and fairness drift?

Action item: Submit your report​

Use the structured form below as your working report. Paste or export it for your organisation’s ethics or governance forum if needed.

Ethical data management report
Research summary

Ethical data management themes, UK GDPR/PECR touchpoints, common bias sources; how lineage, retention and metadata reduce risk.

Your response here...

Bias and error analysis (≥3 sources)

For each source, explain impact on fairness and accuracy of the churn model.

Your response here...

Security and compliance evaluation (≥2 requirements)

Which aspects of current policies should be evaluated for compliance and transparent ML development?

Your response here...

Security protocol design (≥2 protocols)

Include techniques such as anonymisation, pseudonymisation or differential privacy where appropriate.

Your response here...

Bias minimisation in data handling (≥3 strategies)

Reference specific risks from the scenario documentation.

Your response here...

Executive summary and next steps

Ensure all four success-criteria areas are covered with clear, actionable recommendations.

Your response here...