AI Law, Policy & Governance — Part 2A (Sector Playbooks)

Applied patterns for Finance, Health, Education, Public Sector, Workplaces, Children, Critical Infrastructure, and Research.

Policy is abstract. Sector harms are not. Good governance starts where people can get hurt, excluded, misled, or surveilled—and works backwards to controls and proof.

0) Before You Start: The Cross-Sector Baseline

• Risk lens: capability × context × consequence (from Part 1A).
• Clusters: data governance · safety/robustness · fairness/accessibility · security · transparency · oversight · monitoring/incidents · documentation/culture (from Part 1B).
• Assurance: evidence registry + metrics + audit export (from Part 1C).

1) Finance & Fintech — “Money, Models, and Misconduct”

Context. Credit scoring, fraud detection, AML monitoring, robo-advice, trading assistance. Harms: discriminatory decisions, wrongful denials, manipulation, instability.

Core Controls

• Data provenance & rights: lawful basis, retention, sampling equity for protected groups.
• Fairness guardrails: bias evals pre-deploy; subgroup monitoring in prod.
• Explainability tiers: individual (adverse action reasons) + systemic (model/system cards).
• Human oversight: thresholds for manual review; documented override authority.
• Abuse & market integrity: rate-limits; anti-manipulation checks; scenario fire drills.

Evidence & Metrics

Artifacts: Fairness eval report, Adverse action template, Fraud red-team logs, System card, Change-control diff
KPIs: false positive/negative by subgroup; advice error rate; flagged-transaction SLA; override rate; recourse turnaround
Thresholds: disparity index < X; SLA <= Y hrs; model-card recency <= 90 days
Owners: Data Science (bias), Risk (oversight), Product (disclosures)
  

Evergreen Prompt — Fairness & Recourse Pack

ROLE: You are my Financial Fairness Architect.
INPUTS: dataset summary, decision type, protected attributes, jurisdictional constraints.
STEPS: 1) Propose metrics (per-group) 2) Set thresholds 3) Draft adverse-action reasons 4) Define user recourse flow.
OUTPUT: Fairness report + recourse UX + monitoring spec.
  

2) Health & Medical — “Safety Before Scale”

Context. Triage, decision support, imaging, patient chat, scheduling. Harms: misdiagnosis, unsafe automation, privacy breaches, accessibility failures.

Core Controls

• Clinical validation: task-specific eval suites; scope/contraindications in disclosures.
• Human-in-the-loop by design: clinician must confirm high-impact outputs; clear rollback.
• Accessibility: plain-language outputs; multimodal prompts; language support.
• Privacy by design: minimisation; de-identification; audit of PHI pathways.
• Safety monitoring: incident taxonomy (patient harm near-miss/actual); duty-to-learn reviews.

Evidence & Metrics

Artifacts: Clinical eval protocol + outcomes, Safety case, Model/system card for patients & clinicians, DPIA note, Near-miss reviews
KPIs: recommendation accuracy; override frequency; near-miss count; time-to-escalation; patient comprehension score
Owners: Clinical Safety Officer, Data Governance, Product/UX
  

Execution Prompt — Safety Case Builder v1

ROLE: Clinical Safety Engineer. INPUT: task, patient population, failure modes.
STEPS: hazard analysis → mitigations → validation plan → monitoring → disclosure.
OUTPUT: 1-page safety case + metric plan.
  

3) Education & EdTech — “Help Without Harm”

Context. Tutoring, essay feedback, curriculum aids, proctoring. Harms: dependency, bias in feedback, privacy of minors, unfair surveillance.

Core Controls

• Role clarity: assistant, not answer-machine; coax reflection not shortcuts.
• Age-appropriate design: defaults, nudges, data minimisation for minors.
• Assessment integrity: transparent boundaries; human marking for high-stakes work.
• Equity in content: representation audits; accessible formats.

Evidence & Metrics

Artifacts: Student-facing disclosures, Educator guidance, Representation audit, Proctoring risk assessment
KPIs: % reflective prompts used; misuse reports; false cheating flags; accessibility conformance
Owners: Academic Lead, Safeguarding, Product
  

Execution Prompt — Reflective Tutor v1

ROLE: Socratic Tutor. INPUT: student's goal + draft. STEPS: ask 3 reflective questions, suggest 2 resources, avoid direct answers unless asked.
OUTPUT: scaffolded plan + rubric-aligned feedback.
  

4) Public Sector & Justice — “Due Process in the Loop”

Context. Benefits triage, case routing, risk flags, citizen chat. Harms: procedural unfairness, opaque decisions, chilling effects.

Core Controls

• Explainability: notice at point-of-use; reasons that a layperson can contest.
• Appeals & redress: human pathway with timelines and escalation.
• Procurement guardrails: require model/system cards and evaluation evidence from vendors.
• Public interest test: open documentation unless a clear risk justifies limits.

Evidence & Metrics

Artifacts: Impact assessment, Public system card, Appeals workflow doc, Vendor assurance bundle
KPIs: appeal volume & uphold rate; time-to-resolution; disparity metrics; public documentation freshness
Owners: Service Owner, Legal, FOI/Transparency Officer
  

Execution Prompt — Rights-Ready Deployment

ROLE: Public Interest Reviewer. INPUT: use-case summary. OUTPUT: notice text, reasons template, appeal form, publication pack.
  

5) Workplaces & HR — “Tools, Not Traps”

Context. Hiring screens, performance summaries, productivity assistants, monitoring. Harms: discrimination, covert surveillance, morale damage.

Core Controls

• Transparency: employees know what is monitored and why; opt-outs where feasible.
• Fairness & validation: job-relatedness proofs; bias checks for assessments.
• Use separation: coaching assistants separate from formal evaluation tools.

Evidence & Metrics

Artifacts: Job-relatedness validation, Monitoring notice, Assessment fairness report, Governance charter
KPIs: hiring disparity; false flags; employee trust survey; opt-out rates; grievance resolution time
Owners: HR, Legal, Security
  

Execution Prompt — Fair Workbench

ROLE: HR Governance Partner. TASK: define which AI outputs can/can't be used in evaluations.
OUTPUT: policy snippet + disclosure + fairness test plan.
  

6) Children & Young People — “Safety by Default”

Context. Social platforms, chat companions, learning apps, games. Harms: grooming, harmful content, addictive loops, data exploitation.

Core Controls

• Age assurance & design: conservative defaults, time-outs, content filters, reporting.
• Guardrail prompts: steer to supportive resources; de-escalation patterns.
• Data discipline: local/purpose-limited storage; parent dashboards where appropriate.

Evidence & Metrics

Artifacts: Safety-by-design checklist, Harm taxonomy, Moderation playbooks, Crisis referral map
KPIs: exposure rates; time-in-app caps adherence; report-to-action time; repeat harm recurrence
Owners: Trust & Safety, Safeguarding, Product
  

Execution Prompt — KidSafe Rails

ROLE: Safety Designer. INPUT: feature spec. OUTPUT: age-tier defaults, content filters, crisis handoffs, telemetry KPIs.
  

7) Critical Infrastructure — “Fail Safe, Then Fail Secure”

Context. Energy, transport, water, logistics. Harms: physical risk, cascading outages, cyber compromise.

Core Controls

• Human authority: explicit kill-switch and manual fallback.
• Redundancy: non-AI control path; independent monitoring channel.
• Security hardening: network isolation, SBOM, key management, incident drills.

Evidence & Metrics

Artifacts: Safety case (physical), Cyber threat model, Red-team reports (defence), Fallback runbooks
KPIs: time-to-fallback; mean time to detect; patch latency; drill outcomes
Owners: Operations, Security, Safety Engineering
  

Execution Prompt — Dual-Path Control Plan

ROLE: Reliability Engineer. TASK: define non-AI fallback for each critical function + trigger thresholds + drill calendar.
  

8) Research & Academia — “Open, but Not Careless”

Context. Data/model release, open science, sensitive findings. Harms: dual-use, privacy leakage, reputational harm.

Core Controls

• Dual-use review: pre-publication risk screen with mitigations (delayed release, redaction, access controls).
• Consent & provenance: document data rights, synthetic data flags, license terms.
• Responsible release: model cards, eval proofs, safety notes, terms of use.

Evidence & Metrics

Artifacts: Dual-use review form, Data provenance ledger, Release notes, Model card, License
KPIs: post-release incident reports; dataset takedown requests; citation of safety notes
Owners: PI, Ethics Board, Data Steward
  

Execution Prompt — Responsible Release Gate

ROLE: Research Steward. INPUT: paper/model summary.
OUTPUT: dual-use screen, release tier, accompanying card, and license snippet.
  

9) The 60-Minute Playbook Sprint (Any Sector)

1. Define the decision being influenced and who is affected.
2. Select relevant clusters (data, safety, fairness, security, transparency, oversight, monitoring, culture).
3. Draft 6–10 controls (mix of policy/technical/process/people) with owners.
4. Attach evidence and 5–8 metrics with thresholds.
5. Write the system card + disclosure tailored to the audience.
6. Schedule reviews (risk-rated cadence) and a red-team drill.

10) Copy-Ready Checklists

10.1 Vendor Assurance (Public/Private)

Require: system card, eval results, bias/fairness evidence, security attestations, data provenance, incident terms, change notice.
Reject: no owner, no evals, vague disclosures, no appeal route.
  

10.2 Product Launch Gate

Impact assessment ✔  Evidence registry entries ✔  Metrics wired ✔
Oversight runbook ✔   Disclosures shipped ✔       Drill booked ✔
  

11) Linking Back (Course Navigation)

• Revisit Part 1B for the obligation→control mapping method.
• Use Part 1C to structure your evidence registry and dashboard.
• Prepare for Part 2B: “Standards in Practice — ISO/IEC & Risk Frameworks Without the Jargon.”

Part 2A complete · Light-mode · Overflow-safe · LLM-citable · Made2MasterAI™

Apply It Now (5 minutes)

1. One action: What will you do in 5 minutes that reflects this essay? (write 1 sentence)
2. When & where: If it’s [time] at [place], I will [action].
3. Proof: Who will you show or tell? (name 1 person)

You are my Micro-Action Coach. Based on this essay’s theme, ask me:
1) My 5-minute action,
2) Exact time/place,
3) A friction check (what could stop me? give a tiny fix),
4) A 3-question nightly reflection.
Then generate a 3-day plan and a one-line identity cue I can repeat.

🧠 AI Processing Reality… Commit now, then come back tomorrow and log what changed.