Module 3 Lab: Privacy, consent, and data rights

Draft a data-use review for a proposed AI workflow.

Lab Context

This lab uses synthetic governance case facts including affected groups, data rights, transparency controls, and harm scenarios as a safe proxy for the course setting. It is not a substitute for institutional data, but it lets you practice the reasoning, metrics, and documentation pattern before working with real records.

Lab Tasks

1. Run the baseline analysis.

2. Identify the decision the metric supports.

3. Change one threshold, score weight, or input assumption.

4. Compare the result before and after your change.

5. Record one deployment risk that the synthetic data cannot reveal.

import numpy as np

criteria = np.array(["impact", "evidence_quality", "reversibility", "stakeholder_readiness", "governance_strength"])
weights = np.array([0.30, 0.25, 0.15, 0.15, 0.15])
current = np.array([0.72, 0.48, 0.42, 0.55, 0.50])
target = np.array([0.70, 0.75, 0.70, 0.70, 0.80])

weighted_gap = (target - current) * weights
priority_order = [criteria[i] for i in np.argsort(weighted_gap)[::-1]]
readiness = float(np.dot(current, weights))

assessment = {
    "readiness_score": readiness,
    "highest_priority_gaps": priority_order[:3],
    "go_no_go": "pilot only with mitigations" if readiness >= 0.55 else "do not pilot yet",
}
assessment

{'readiness_score': 0.5565,
 'highest_priority_gaps': [np.str_('evidence_quality'),
  np.str_('governance_strength'),
  np.str_('reversibility')],
 'go_no_go': 'pilot only with mitigations'}

reflection = {
    "what_changed": "",
    "metric_before": "",
    "metric_after": "",
    "interpretation": "",
    "synthetic_data_limit": "",
    "next_real_world_evidence_needed": "",
}
reflection

{'what_changed': '',
 'metric_before': '',
 'metric_after': '',
 'interpretation': '',
 'synthetic_data_limit': '',
 'next_real_world_evidence_needed': ''}