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Skills application solution

Instruction and application
Complete

Skills application solution​

Compare your skills application output to the solution example below provided by Multiverse subject matter experts.

Solution 1. Regulatory and ethical analysis

Regulation: FERPA (Family Educational Rights and Privacy Act – US)Impact: FERPA protects the privacy of student education records and requires that PII is only used for authorised educational purposes. In this project, the ML pipeline must ensure student data is anonymised or pseudonymised, access is restricted to approved personnel, and parental consent is obtained when applicable.2. Governance framework application

Selected framework: SAFE-D

  • Fairness: SAFE-D encourages reviewing model outcomes across different student subgroups to ensure that predictive performance does not disadvantage students based on demographic characteristics. This could include running disaggregated accuracy checks for ethnicity or socio-economic status.

  • Human oversight: SAFE-D promotes integrating educators and data ethics stakeholders into the review process, ensuring that predictions are interpreted responsibly and not used in isolation to make decisions about students.3. Compliant data strategy

  • Data minimisation: Limit features used in the model to those that have a direct correlation with educational performance (e.g., attendance, past academic performance), and exclude highly sensitive fields like disciplinary history unless justified.

  • Metadata tracking: Tag each dataset with source, access rights, collection date, and transformation logs to support traceability and reproducibility.

  • Retention limits: Implement a 12-month retention policy for training data, with automatic review before renewal or deletion.

  • Access management: Use role-based access controls to restrict sensitive data to only essential team members. Maintain an audit log of all access, changes, and approvals related to data handling and model updates.4. Data quality and fairness plan

  • Quality check 1: Detect missing values and enforce threshold alerts when key fields (e.g., attendance or grades) fall below 95% completeness.

  • Quality check 2: Standardise categorical fields such as school district names to ensure consistency.

  • Fairness check: Monitor model accuracy across student groups by ethnicity and gender. If disparities exceed a set threshold (e.g., 5% difference in precision or recall), trigger a review.

  • Bias response: Document all fairness evaluations and include notes in the model card. Retrain the model with balanced samples or introduce reweighting where needed to correct disparities.

What this example does well​

  • Applies a relevant regulation with a clear explanation of how it influences data handling decisions.
  • Connects governance principles directly to fairness and oversight in the ML lifecycle.
  • Presents actionable, realistic steps that demonstrate understanding of data minimisation, metadata, and audit-readiness.

Tips for applying this skill in your role​

  • Start by mapping out all data sources and types before making governance decisions—this helps surface risks early.
  • Use simple tools (like checklists or model cards) to document fairness and data quality checks so they’re easy to review and maintain.
  • Collaborate with non-technical stakeholders (e.g., legal, ethics, educators) when designing governance strategies—this builds trust and accountability.

Action item: Reflection​

Compare your output to the solution example provided.

  • What did you do well?
  • Where could you improve?
  • How would your strategy need to evolve if the model were reused for a different population or region?