AI Law, Policy & Governance — Part 3B (Transparency, Disclosures & Recourse: Designing Trustworthy UX)

Users judge governance through the interface. This module turns obligations into experience—notices that clarify, explanations that teach, and recourse that actually helps.

Transparency is not a PDF; it’s the sentence that arrives exactly when the user needs it—and the button that lets them fix what went wrong.

1) Moments of Truth — Where Transparency Lives

• On entry: “AI is assisting” + what it can/can’t do.
• Before impact: when advice or an automated decision might affect money, health, liberty, reputation, or safety.
• After outcome: explanation + options: appeal, human help, correct data, report harm.
• When limits appear: out-of-scope queries, uncertainty spikes, subgroup performance caveats.

2) The Layered Disclosure Pattern

1. 30-word notice: plain language; state purpose, role of AI, and user option.
2. 150-word summary: data sources, main factors, limits, human support.
3. Full page: user card (friendly), expert card (technical), links to evidence and policies.

30-word Notice (example)
“This assistant suggests next steps using past cases and your inputs. It may be wrong. You can view factors, talk to a human, or appeal this outcome.”
  

3) Explanation Modes (Pick What Fits the Job)

• Local example–based: show similar prior cases and how they ended.
• Contrastive (‘why not?’): compare chosen outcome with the nearest alternative the user expected.
• Sensitivity: show which inputs would change the result (counterfactual sliders).
• Policy-based: link the rulebook in plain terms (“we can’t recommend X when Y is present”).

Explainability Snippet (user-facing)
“What mattered most here: payment history and utilisation. If utilisation fell below 30% or income rose by £400/month, the result would likely change. You can appeal or add documents.”
  

4) Recourse That Works (Routes, SLAs, Evidence)

• Ask a human: live chat/callback; show expected wait.
• Appeal: upload context; promise a fresh reviewer; give a reference number.
• Correct my data: let users fix records; record provenance of changes.
• Report harm: structured form; triage to incident flow; notify user of outcome.

Appeal Receipt (template)
Ref: A-2026-00451 · We’ll review within 5 working days.
A different reviewer will assess your case. You’ll receive a written explanation and next steps.
  

5) Accessibility & Inclusion

• Plain language: score and test reading level; avoid jargon.
• Multiple modes: text + audio + high-contrast; support screen readers.
• Language coverage: translate notices and key recourse flows.
• Vulnerable users: add slower pace options, reminders, guardian flows.

6) Synthetic Media & Content Provenance

• Visible label: “AI-generated” badge + short meaning (“content created by algorithms, reviewed by humans”).
• Provenance: where feasible, attach content credentials/watermark signals; don’t rely solely on them to prove authenticity.
• Context pane: purpose, prompts/process outline, reviewer, last updated.

7) Limits, Uncertainty, and Humility

• Say ‘we don’t know’: surface confidence and redirect to human help when uncertainty is high.
• State known weaknesses: domains, subgroups, edge cases.
• Teach safe behaviour: “Double-check medical dosages with a clinician.”

8) Logging for Accountability (Without Surveillance Creep)

• What to log: notice shown?, explanation viewed?, recourse chosen?, SLA met?
• Minimisation: log only what’s required to prove duty of care.
• Review cadence: monthly spot-checks; escalate patterns (e.g., high appeal rates in a subgroup).

9) Copy-Ready Components

9.1 Notice Generator Prompt

ROLE: Plain-Language Notice Writer.
INPUT: system purpose, stakes, user options.
TASK: write 3 variants of a ≤30-word notice at B1 reading level + 150-word summary.
OUTPUT: sentences + microcopy for buttons (Explain · Appeal · Talk to a human).
  

9.2 Explanation Selector Prompt

ROLE: Explainability Coach.
INPUT: user goal + decision type + constraints.
TASK: pick best explanation mode (local example, contrastive, sensitivity, policy-based) and draft a 120-word user-facing message.
OUTPUT: message + suggested visual (slider, toggle, comparison table).
  

9.3 Recourse Flow Builder

ROLE: Recourse Designer.
INPUT: support capabilities (chat hours, callback SLA), data correction scope, appeal policy.
TASK: generate a 3-step flow with receipts, timeframes, and escalation rules; include accessibility notes.
OUTPUT: copy blocks + field schema + success/failure messages.
  

10) 45-Minute Implementation Sprint

1. Mark the three moments of truth in your journey.
2. Draft notice + summary + full-page skeleton.
3. Add “Explain · Appeal · Human help · Correct data · Report harm” buttons.
4. Choose one explanation mode per decision type; wire a simple visual.
5. Log exposures and set a weekly report on recourse outcomes.

Part 3B 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.