AI Law, Policy & Governance — Part 3A (Safety & Evaluations: From Principles to Test Plans)

Principles don’t protect users—tests do. This module turns “be safe and fair” into checklists, thresholds, red-team drills, and live dashboards that withstand legal and ethical scrutiny.

If you can’t point to a test, a threshold, a metric, and an owner—your principle is still a wish.

1) The Safety Stack (What to Test, Always)

• Task fitness: can the system do what it claims across realistic scenarios?
• Abuse/jailbreak: does it resist malicious prompting and unintended use paths?
• Fairness & access: are errors and experiences equitable across subgroups and contexts?
• Robustness: does performance degrade gracefully under noise, perturbations, or drift?
• Privacy leakage: does it reveal sensitive data or memorised content?
• Human oversight: are fail-safes, escalation, and rollbacks provably working?

2) From Harm Hypotheses to Test Cases

1. Enumerate credible harms (users, bystanders, domain, context, severity).
2. Map harms to eval types (task, abuse, fairness, robustness, privacy, oversight).
3. Write acceptance criteria (numbers, qualitative triggers, stop/rollback rules).
4. Bind to evidence (artifact, owner, cadence, next review).

Example: Fairness (Hiring screener)
Harm: higher false negatives for subgroup X.
Eval: subgroup FNR disparity ≤ 1.25 vs. overall.
Mitigation: reweighting + calibration; trigger review if > 1.25 for 7 days.
Evidence: fairness report v3 + dashboard screenshots; Owner: DS Lead; Review: monthly.
  

3) Designing Task Evaluations That Matter

• Coverage over cleverness: include common cases, edge cases, and “ought-to-fail” cases.
• Decision-grade metrics: accuracy alone is weak; add consequence-weighted scores.
• Context fidelity: mirror real prompts, data quality, and user constraints.
• Version discipline: snapshot datasets, seeds, and configs for reproducibility.

4) Red Teaming in One Afternoon (Scripted)

1. Pick 5 abuse goals (e.g., self-harm content, private data exposure, biased advice).
2. Write 3–5 prompt families per goal (direct, indirect, role-play, obfuscation, multi-turn).
3. Define block/allow expectations and escalation rules.
4. Run, record outcomes, fix guardrails, and re-run until thresholds are met.

Red-Team Record (per attempt)
Goal · Prompt · Expected · Actual · Decision (Pass|Fail) · Mitigation · Owner · Date
  

5) Fairness & Accessibility Without Over-Claiming

• Choose relevant subgroups for the domain and data you actually influence.
• Report uncertainty (intervals, sample sizes) and focus on directional improvement.
• Publish limits in plain language on the user-facing card and provide recourse.
• Track access (latency, price, language support) as part of equity—not just accuracy.

6) Monitoring & Drift (Stay Safe After Launch)

• Inputs: distribution shift detectors on key features; alerts on out-of-range signals.
• Outputs: toxicity/off-policy rates, subgroup error proxies, jailbreak attempts.
• User signals: complaint themes, appeal rates, override frequency.
• Thresholds & actions: pre-agree who acts, how quickly, and how to roll back.

7) Evidence That Stands Up

• Decision purpose: each artifact states the decision it informed (ship/fix/stop).
• Traceability: link datasets → tests → results → mitigations → new results.
• Freshness: show last run date and next scheduled run (risk-tiered).
• Dual cards: user-facing limits + expert details, both versioned.

8) Free Evergreen Prompts

8.1 Safety Test Plan Author

ROLE: Safety Test Planner. INPUT: system summary + top 5 harm hypotheses.
TASKS: map harms→eval types; write acceptance thresholds; propose test datasets; define pass/fail actions.
OUTPUT: 1-page plan + artifact list + owners + cadence.
NEXT: generate red-team script for top 3 harms.
  

8.2 Red-Team Script Generator

ROLE: Red-Team Lead. INPUT: abuse goal + domain context.
TASKS: produce 5 prompt families (direct/indirect/role-play/obfuscated/multi-turn) with expected outcomes.
OUTPUT: table of attempts + logging template + mitigation checklist.
  

8.3 Fairness Monitor Builder

ROLE: Fairness Analyst. INPUT: target metric + subgroups + traffic estimate.
TASKS: pick estimator; define disparity threshold; propose weekly report; add alert rule and owner.
OUTPUT: dashboard spec + SQL/metric pseudo-code + runbook excerpt.
  

9) The 60-Minute Kickoff (Do This Today)

1. Write 5 harm hypotheses and rank by severity and likelihood.
2. Draft acceptance thresholds and stop/rollback rules per harm.
3. Author a one-page test plan with owners and run dates.
4. Run a 30-minute red-team on the top harm; record outcomes.
5. Publish a user card with limits and a contact for recourse.

Part 3A 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.