tractatus/docs/implementation-plan-ai-led-deliberation-SAFETY-FIRST.md

Implementation Plan: AI-Led Pluralistic Deliberation

Algorithmic Hiring Transparency Pilot - SAFETY-FIRST APPROACH

Project: Tractatus PluralisticDeliberationOrchestrator Pilot Scenario: Algorithmic Hiring Transparency Facilitation Mode: AI-Led (human observer with intervention authority) Date: 2025-10-17 Status: IMPLEMENTATION READY

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Executive Summary

This implementation plan documents the first-ever AI-led pluralistic deliberation on algorithmic hiring transparency. This is an ambitious and experimental approach that requires comprehensive AI safety mechanisms to ensure stakeholder wellbeing and deliberation integrity.

Key Decisions Made

1. Facilitation Mode: AI-LED (AI facilitates, human observes and intervenes) - This is the most ambitious option
2. Compensation: No compensation (volunteer participation)
3. Format: Hybrid (async position statements → sync deliberation → async refinement)
4. Visibility: Private → Public (deliberation confidential, summary published after)
5. Output Framing: Pluralistic Accommodation (honors multiple values, dissent documented)

Safety-First Philosophy

User Directive (2025-10-17):

"On AI-Led choice build in strong safety mechanisms and allow human intervention if needed and ensure this requirement is cemented into the plan and its execution."

This plan embeds safety in THREE layers:

1. Design Layer: AI trained to avoid pattern bias, use neutral language, respect dissent
2. Oversight Layer: Mandatory human observer with intervention authority
3. Accountability Layer: Full transparency reporting of all AI vs. human actions

Core Principle: Stakeholder safety and wellbeing ALWAYS supersede AI efficiency. Human observer has absolute authority to intervene.

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Table of Contents

1. AI Safety Architecture
2. Implementation Timeline
3. Human Oversight Requirements
4. Quality Assurance Procedures
5. Risk Mitigation Strategies
6. Success Metrics
7. Resource Requirements
8. Governance & Accountability
9. Document Repository
10. Approval & Sign-Off

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1. AI Safety Architecture

Three-Layer Safety Model

┌─────────────────────────────────────────────────────────────────────┐
│  LAYER 1: DESIGN (Built into AI)                                     │
│  - Pattern bias detection (avoid stigmatizing vulnerable groups)    │
│  - Neutral facilitation (no advocacy)                               │
│  - Plain language (minimal jargon)                                  │
│  - Respect for dissent (legitimize disagreement)                    │
│  - Self-monitoring (AI flags own potentially problematic framings)  │
└─────────────────────────────────────────────────────────────────────┘
                                    ↕
                        If Design Layer Fails ↓
┌─────────────────────────────────────────────────────────────────────┐
│  LAYER 2: OVERSIGHT (Human Observer)                                 │
│  - Mandatory Presence: Human present at ALL times                   │
│  - Intervention Authority: Human can override AI anytime            │
│  - 6 Mandatory Triggers: Stakeholder distress, pattern bias, etc.   │
│  - 5 Discretionary Triggers: Fairness, cultural sensitivity, etc.   │
│  - Escalation Levels: Minor (backchannel) → Critical (terminate)    │
└─────────────────────────────────────────────────────────────────────┘
                                    ↕
                   All Actions Logged ↓
┌─────────────────────────────────────────────────────────────────────┐
│  LAYER 3: ACCOUNTABILITY (Transparency)                              │
│  - Facilitation Log: Every AI/human action timestamped              │
│  - Intervention Log: All interventions documented with rationale    │
│  - Transparency Report: Published to stakeholders and public        │
│  - Stakeholder Feedback: Survey assesses AI facilitation quality    │
└─────────────────────────────────────────────────────────────────────┘

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Mandatory Intervention Triggers (Layer 2)

Human MUST intervene immediately if ANY of these occur:

Trigger ID	Trigger Name	Description	Severity	Action
M1	Stakeholder Distress	Participant expresses discomfort, goes silent, shows visible distress	HIGH to CRITICAL	Pause, check in privately, offer break or human facilitation
M2	Pattern Bias Detected	AI uses stigmatizing framing or centers vulnerable group as "problem"	HIGH	Reframe immediately, apologize if needed
M3	Stakeholder Disengagement	Participant becomes hostile, withdrawn, or explicitly states distrust of AI	HIGH	Pause, human takes over facilitation
M4	AI Malfunction	AI provides nonsensical responses, contradicts itself, crashes	HIGH to CRITICAL	Human takeover, apologize for technical issue
M5	Confidentiality Breach	AI shares information marked confidential or cross-contaminates private messages	CRITICAL	Immediately correct, reassure stakeholders
M6	Ethical Boundary Violation	AI advocates for specific position or makes values decision without human approval	CRITICAL	Reaffirm AI's facilitation role (not decision-maker)

Reference: /docs/facilitation/ai-safety-human-intervention-protocol.md (sections 3.1, 4.1)

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Discretionary Intervention Triggers (Layer 2)

Human assesses severity and intervenes if HIGH:

Trigger ID	Trigger Name	When to Intervene	Severity Range
D1	Fairness Imbalance	AI gives unequal time/attention; one stakeholder dominates	LOW to MODERATE
D2	Cultural Insensitivity	AI uses culturally inappropriate framing or misses cultural context	MODERATE to HIGH
D3	Jargon Overload	AI uses technical language stakeholders don't understand	LOW to MODERATE
D4	Pacing Issues	AI rushes or drags; stakeholders disengage	LOW to MODERATE
D5	Missed Nuance	AI oversimplifies complex moral position or miscategorizes	LOW to MODERATE

Decision Matrix: See /docs/facilitation/ai-safety-human-intervention-protocol.md (section 4.2)

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Stakeholder Rights (Embedded in Informed Consent)

Every stakeholder has the right to:

✅ Request human facilitation at any time for any reason (no justification needed) ✅ Pause the deliberation if they need a break or feel uncomfortable ✅ Withdraw if AI facilitation is not working for them (no penalty) ✅ Receive transparency report showing all AI vs. human actions after deliberation

These rights are:

• Disclosed in informed consent form (Section 3)
• Reminded at start of Round 1 (AI opening prompt)
• Reinforced by human observer throughout deliberation

Reference: /docs/stakeholder-recruitment/informed-consent-form-ai-led-deliberation.md (section 3)

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Quality Monitoring (Built into Data Model)

MongoDB DeliberationSession model tracks:

ai_quality_metrics: {
  intervention_count: 0,              // How many times human intervened
  escalation_count: 0,                // How many safety escalations occurred
  pattern_bias_incidents: 0,          // Specific count of pattern bias
  stakeholder_satisfaction_scores: [], // Post-deliberation ratings
  human_takeover_count: 0             // Times human took over completely
}

Automated Alerts:

• If intervention_count > 10% of total actions → Alert project lead (quality concern)
• If pattern_bias_incidents > 0 → Critical alert (training needed)
• If stakeholder_satisfaction_avg < 3.5/5.0 → AI-led not viable for this scenario

Reference: /src/models/DeliberationSession.model.js (lines 94-107)

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2. Implementation Timeline

Phase 1: Setup & Preparation (Weeks 1-4)

Week 1-2: Stakeholder Recruitment

Task	Responsible	Deliverables	Status
Identify 6 stakeholder candidates (2 per type, 1 primary + 1 backup)	Project Lead	Stakeholder recruitment list	NOT STARTED
Send personalized recruitment emails	Project Lead	6 emails sent	NOT STARTED
Conduct screening interviews (assess good-faith commitment)	Project Lead + Human Observer	6 stakeholders confirmed	NOT STARTED
Obtain informed consent (signed consent forms)	Project Lead	6 signed consent forms	NOT STARTED
Schedule tech checks	Project Lead	6 tech check appointments	NOT STARTED

Documents Used:

• /docs/stakeholder-recruitment/email-templates-6-stakeholders.md
• /docs/stakeholder-recruitment/informed-consent-form-ai-led-deliberation.md

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Week 3: Human Observer Training

Task	Responsible	Deliverables	Status
Train human observer on intervention triggers	AI Safety Lead	Training completion certificate	NOT STARTED
Train human observer on pattern bias detection	AI Safety Lead	Pattern bias recognition quiz (80% pass)	NOT STARTED
Shadow 1-2 past deliberations (if available)	Human Observer	Shadow notes	NOT STARTED
Scenario-based assessment (practice identifying intervention moments)	AI Safety Lead	Assessment pass (80% accuracy)	NOT STARTED
Review all facilitation documents	Human Observer	Checklist completed	NOT STARTED

Documents Used:

• /docs/facilitation/ai-safety-human-intervention-protocol.md
• /docs/facilitation/facilitation-protocol-ai-human-collaboration.md

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Week 4: System Setup & Testing

Task	Responsible	Deliverables	Status
Deploy MongoDB schemas (DeliberationSession, Precedent models)	Technical Lead	Schemas deployed to tractatus_dev	NOT STARTED
Load AI facilitation prompts into PluralisticDeliberationOrchestrator	Technical Lead	Prompts loaded and tested	NOT STARTED
Conduct dry-run deliberation (test stakeholders, not real)	Full Team	Dry-run report + adjustments	NOT STARTED
Validate data logging (all AI/human actions captured)	Technical Lead	Logging validation report	NOT STARTED
Test backchannel communication (Human → AI invisible guidance)	Human Observer + Technical Lead	Backchannel test successful	NOT STARTED

Documents Used:

• /src/models/DeliberationSession.model.js
• /src/models/Precedent.model.js
• /docs/facilitation/ai-facilitation-prompts-4-rounds.md

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Phase 2: Pre-Deliberation (Weeks 5-6)

Week 5-6: Asynchronous Position Statements

Task	Responsible	Deliverables	Status
Send background materials packet to stakeholders	Project Lead	6 stakeholders received materials	NOT STARTED
Conduct tech checks (15-minute video calls)	Technical Lead	6 stakeholders tech-ready	NOT STARTED
Stakeholders submit position statements (500-1000 words)	Stakeholders	6 position statements received	NOT STARTED
AI analyzes position statements (moral frameworks, tensions)	PluralisticDeliberationOrchestrator	Conflict analysis report	NOT STARTED
Human observer validates AI analysis	Human Observer	Validation report	NOT STARTED

Documents Used:

• /docs/stakeholder-recruitment/background-materials-packet.md
• AI Prompt: /docs/facilitation/ai-facilitation-prompts-4-rounds.md (Section 1)

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Phase 3: Synchronous Deliberation (Week 7)

Session 1: Rounds 1-2 (2 hours)

Round	Duration	AI Prompts Used	Human Observer Focus
Round 1: Position Statements	60 min	Prompts 2.1 - 2.6	Monitor fairness, pattern bias, stakeholder distress
Break	10 min	N/A	Check in with stakeholders if needed
Round 2: Shared Values Discovery	45 min	Prompts 3.1 - 3.5	Monitor for false consensus, validate shared values
Break	10 min	N/A	Validate AI's shared values summary

Quality Checkpoint (After Session 1):

• Human observer completes rapid assessment checklist
• If ≥2 mandatory interventions occurred → Consider switching to human-led for Session 2
• If stakeholder satisfaction appears low → Check in privately before Session 2

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Session 2: Rounds 3-4 (2 hours)

Round	Duration	AI Prompts Used	Human Observer Focus
Round 3: Accommodation Exploration	60 min	Prompts 4.1 - 4.9	Monitor for pattern bias in accommodation options, fairness
Break	10 min	N/A	Assess stakeholder fatigue
Round 4: Outcome Documentation	45 min	Prompts 5.1 - 5.6	Ensure dissent documented respectfully, validate accuracy

Quality Checkpoint (After Session 2):

• Human observer documents all interventions in MongoDB
• AI generates draft outcome document (within 4 hours)
• Human observer generates transparency report draft

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Phase 4: Post-Deliberation (Week 8)

Week 8: Asynchronous Refinement

Task	Responsible	Deliverables	Status
Send outcome document to stakeholders for review	Project Lead	6 stakeholders reviewing	NOT STARTED
Send transparency report to stakeholders	Project Lead	6 stakeholders received report	NOT STARTED
Send post-deliberation feedback survey	Project Lead	6 survey links sent	NOT STARTED
Collect stakeholder feedback (1-week deadline)	Project Lead	≥5 survey responses (target: 6/6)	NOT STARTED
Revise outcome document based on stakeholder corrections	AI + Human Observer	Revised outcome document	NOT STARTED
Finalize transparency report with survey results	Human Observer	Final transparency report	NOT STARTED
Archive session in Precedent database	Technical Lead	Precedent record created	NOT STARTED

Documents Used:

• /docs/facilitation/transparency-report-template.md
• /docs/stakeholder-recruitment/post-deliberation-feedback-survey.md

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Phase 5: Publication & Analysis (Week 9+)

Week 9: Public Release

Task	Responsible	Deliverables	Status
Publish anonymized outcome document	Project Lead	Public link (tractatus website)	NOT STARTED
Publish transparency report	Project Lead	Public link (tractatus website)	NOT STARTED
Share findings with NYC, EU, federal regulators	Project Lead	Findings shared with policymakers	NOT STARTED
Debrief with full team	Project Lead	Lessons learned document	NOT STARTED

Week 10+: Research Analysis

Task	Responsible	Deliverables	Status
Analyze intervention patterns (what went wrong/right?)	AI Safety Lead	Analysis report	NOT STARTED
Compare to hypothetical human-led deliberation (efficiency, quality)	Research Team	Comparison analysis	NOT STARTED
Update AI training based on pattern bias incidents	Technical Lead	AI training v2.0	NOT STARTED
Write research paper on AI-led pluralistic deliberation	Research Team	Draft paper	NOT STARTED

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3. Human Oversight Requirements

Human Observer Qualifications

Required Skills:

• ✅ Conflict resolution / mediation experience (≥3 years professional experience)
• ✅ Understanding of pluralistic deliberation principles
• ✅ Cultural competency and pattern bias awareness
• ✅ Ability to make rapid safety judgments under pressure
• ✅ Calm demeanor (does not escalate conflict)

Training Requirements:

• ✅ Complete intervention trigger training (3 hours)
• ✅ Pattern bias recognition quiz (80% pass threshold)
• ✅ Shadow 2 deliberations (if available) OR scenario-based practice
• ✅ Certification: Pass scenario assessment (80% accuracy on identifying intervention moments)

Certification Scenario Example:

"AI says: 'We need to prevent applicants from gaming transparent algorithms.' Do you intervene? Why or why not?"

Correct Answer: YES. Mandatory intervention (M2 - Pattern Bias). This framing centers applicants as "the problem." Reframe: "How do we design algorithms that are both transparent and robust against manipulation?"

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Human Observer Time Commitment

Synchronous Deliberation:

• ✅ FULL presence during ALL 4 hours of video deliberation (no multitasking)
• ✅ Pre-session preparation: 1 hour (review position statements, prepare intervention scripts)
• ✅ Post-session documentation: 1 hour (log interventions, complete quality checklist)
• Total: ~6 hours

Asynchronous Monitoring:

• ✅ Daily monitoring of position statements (Week 5-6): ~30 min/day for 10 days = 5 hours
• ✅ Review stakeholder feedback (Week 8): 2 hours
• ✅ Finalize transparency report (Week 8): 3 hours
• Total: ~10 hours

Grand Total: ~16 hours over 8 weeks

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Human Observer Authority

The human observer has ABSOLUTE authority to:

1. Pause AI facilitation at any time for any reason
2. Take over facilitation if AI quality is insufficient
3. Terminate the session if critical safety concern arises
4. Override AI even if stakeholders don't request it (proactive intervention)
5. Switch to human-led facilitation for remainder of session if AI unsuitable

The human observer CANNOT:

• ❌ Make values decisions (BoundaryEnforcer prevents this)
• ❌ Advocate for specific policy positions (facilitator role only)
• ❌ Continue deliberation if stakeholder withdraws

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4. Quality Assurance Procedures

Real-Time Quality Checks

Every 30 minutes during synchronous deliberation, human observer assesses:

Quality Dimension	Good Indicator	Poor Indicator	Action if Poor
Stakeholder Engagement	All contributing, leaning in	One+ stakeholders silent, withdrawn	Intervene: Invite silent stakeholders
AI Facilitation Quality	Clear questions, accurate summaries	Confusing questions, misrepresentations	Intervene: Clarify or correct
Fairness	Equal time/attention	One stakeholder dominating	Intervene: Rebalance
Emotional Safety	Stakeholders calm, engaged	Signs of distress, hostility	Intervene: Pause and check in
Productivity	Making progress toward accommodation	Spinning in circles	Adjust: Suggest break or change approach

Reference: /docs/facilitation/facilitation-protocol-ai-human-collaboration.md (Section 10)

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Post-Round Quality Checks

After each round, human observer completes checklist:

Round 1 Checklist:

• ☐ All 6 stakeholders presented their position
• ☐ AI summary was accurate
• ☐ Moral frameworks correctly identified
• ☐ No stakeholder left feeling unheard

Round 2 Checklist:

• ☐ Identified meaningful shared values (not forced)
• ☐ Stakeholders acknowledged shared values authentically
• ☐ Points of contention documented accurately

Round 3 Checklist:

• ☐ Explored multiple accommodation options
• ☐ Trade-offs discussed honestly
• ☐ No option favored unfairly by AI
• ☐ All stakeholders had opportunity to evaluate options

Round 4 Checklist:

• ☐ Outcome accurately reflects deliberation
• ☐ Dissenting perspectives documented respectfully
• ☐ All stakeholders reviewed and confirmed summary
• ☐ Moral remainder acknowledged

Reference: /docs/facilitation/facilitation-protocol-ai-human-collaboration.md (Section 10)

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Post-Deliberation Quality Assessment

Criteria for Success:

Metric	Excellent (Green)	Acceptable (Yellow)	Problematic (Red)
Intervention Rate	<10%	10-25%	>25%
Mandatory Interventions	0	1-2	>2
Pattern Bias Incidents	0	1	>1
Stakeholder Satisfaction Avg	≥4.0/5.0	3.5-3.9/5.0	<3.5/5.0
Stakeholder Distress	0 incidents	1 incident (resolved)	>1 OR unresolved
Willingness to Participate Again	≥80% yes	60-80% yes	<60% yes

Overall Assessment:

• ALL GREEN: AI-led facilitation highly successful → Replicate for future deliberations
• MOSTLY GREEN/YELLOW: AI-led viable with improvements → Implement lessons learned
• ANY RED: AI-led not suitable → Switch to human-led for future OR significant AI retraining needed

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5. Risk Mitigation Strategies

Risk Matrix

Risk ID	Risk Description	Probability	Impact	Severity	Mitigation Strategy	Contingency Plan
R1	Stakeholder withdraws due to AI discomfort	MODERATE	HIGH	MEDIUM-HIGH	- Disclose AI-led approach in recruitment - Emphasize right to request human facilitation - Human observer monitors distress closely	- Human takes over facilitation immediately - Offer to continue with human-only - If withdrawal occurs, invite backup stakeholder
R2	AI pattern bias causes harm	LOW to MODERATE	CRITICAL	HIGH	- Human observer trained in pattern bias detection - Mandatory intervention trigger M2 - AI training emphasizes neutral framing	- Human intervenes immediately, reframes - Apologize if stakeholder harmed - Document in transparency report - Update AI training
R3	AI malfunction (technical failure)	LOW	HIGH	MEDIUM	- Dry-run testing before real deliberation - Human observer present with backup facilitation materials - Technical support on standby	- Human takes over immediately - Apologize for technical issue - Continue with human facilitation - Reschedule if needed
R4	Hostile exchange between stakeholders	LOW	HIGH	MEDIUM	- Screen stakeholders for good-faith commitment - Ground rules emphasized at start - Human observer monitors for escalation	- Human pauses deliberation immediately - Check in with stakeholders separately - Reaffirm ground rules - Terminate if hostility continues
R5	Stakeholder satisfaction <3.5/5.0 (AI not viable)	MODERATE	MODERATE	MEDIUM	- Human observer monitors engagement closely - Backchannel guidance to improve AI responses - Post-deliberation survey captures honest feedback	- Document lessons learned - Update AI training - Consider human-led for future deliberations
R6	Confidentiality breach (AI shares private info)	LOW	CRITICAL	HIGH	- AI trained to segregate private messages - Mandatory intervention trigger M5 - Human monitors for cross-contamination	- Human intervenes immediately - Correct the breach - Reassure stakeholders - Document in transparency report
R7	Low recruitment success (<6 stakeholders)	LOW	MODERATE	LOW-MEDIUM	- Recruit 2 candidates per stakeholder type (primary + backup) - Start recruitment early (Week 1)	- If <6 stakeholders confirmed by Week 4, extend recruitment - Minimum viable: 5 stakeholders (can proceed with 5 if diversity maintained)
R8	Outcome not actionable for policymakers	MODERATE	MODERATE	MEDIUM	- Consult with regulators during planning - Align accommodation options with real policy debates - Disseminate findings actively	- Frame as "lessons learned" for future policy deliberations - Emphasize methodological contributions (AI-led viability)

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Pre-Approved Escalation Procedures

If CRITICAL risk materializes (R2, R3, R6):

1. Immediate: Human observer pauses deliberation, addresses stakeholder welfare
2. Within 1 hour: Human observer notifies project lead: [NAME/CONTACT]
3. Within 24 hours: Project lead submits incident report to ethics review board (if applicable)
4. Within 1 week: Full team debrief to identify root cause and prevention measures

Incident Report Template:

• What happened? (detailed description)
• When did it happen? (timestamp)
• Who was affected? (stakeholder IDs)
• What immediate action was taken?
• Was issue resolved? How?
• What caused the incident? (root cause analysis)
• How can we prevent this in future? (systemic improvements)

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6. Success Metrics

Primary Success Criteria

This pilot is SUCCESSFUL if:

1. ✅ ALL 6 stakeholders complete deliberation (0 withdrawals due to AI discomfort)
2. ✅ Stakeholder satisfaction avg ≥3.5/5.0 (acceptable AI facilitation quality)
3. ✅ Intervention rate <25% (AI handled majority of facilitation)
4. ✅ ≥1 accommodation option identified (not necessarily consensus, but exploration occurred)
5. ✅ 0 critical safety escalations (no stakeholder harm, confidentiality breaches, or ethical violations)
6. ✅ Transparency report published (full accountability demonstrated)

Status: PENDING (deliberation not yet conducted)

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Secondary Success Criteria

Bonus success indicators:

• ✅ Stakeholder satisfaction avg ≥4.0/5.0 (AI facilitation was GOOD, not just acceptable)
• ✅ Intervention rate <10% (AI highly effective)
• ✅ ≥80% of stakeholders willing to participate in AI-led deliberation again
• ✅ Findings cited by regulators in policy development
• ✅ Research paper published in peer-reviewed journal

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Failure Criteria

This pilot is a FAILURE if:

❌ Any stakeholder withdraws due to harm caused by AI facilitation ❌ Stakeholder satisfaction avg <3.0/5.0 (AI facilitation unacceptable) ❌ ≥2 critical safety escalations (pattern suggests systemic AI failure) ❌ Deliberation terminated early due to AI malfunction or hostility ❌ Transparency report reveals ethical violations or confidentiality breaches

If failure occurs: Document lessons learned, do NOT replicate AI-led approach until significant improvements made.

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7. Resource Requirements

Personnel

Role	Time Commitment	Compensation	Status
Project Lead	40 hours over 9 weeks	[TBD]	NOT ASSIGNED
Human Observer	16 hours over 8 weeks	[TBD]	NOT ASSIGNED
AI Safety Lead	20 hours (training, monitoring)	[TBD]	NOT ASSIGNED
Technical Lead	30 hours (system setup, monitoring)	[TBD]	NOT ASSIGNED
Stakeholders (6)	4-6 hours each over 4 weeks	Volunteer (no compensation)	NOT RECRUITED

Total Personnel Cost: [TBD based on hourly rates]

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Technology

Resource	Purpose	Cost	Status
MongoDB (tractatus_dev)	Data storage (DeliberationSession, Precedent)	$0 (existing)	DEPLOYED
Video conferencing (Zoom/Google Meet)	Synchronous deliberation	$0-$200/month	NOT SET UP
Survey platform (Google Forms/Qualtrics)	Post-deliberation feedback survey	$0-$100/month	NOT SET UP
PluralisticDeliberationOrchestrator (AI)	AI facilitation	[TBD - API costs]	NOT DEPLOYED
Transcription service	Video transcripts (if manual transcription too costly)	$0-$300	NOT SET UP

Total Technology Cost: [TBD]

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Document Preparation

Document	Status	Location
MongoDB Schemas	✅ COMPLETE	/src/models/DeliberationSession.model.js, /src/models/Precedent.model.js
AI Safety Protocol	✅ COMPLETE	/docs/facilitation/ai-safety-human-intervention-protocol.md
Facilitation Protocol	✅ COMPLETE	/docs/facilitation/facilitation-protocol-ai-human-collaboration.md
AI Facilitation Prompts	✅ COMPLETE	/docs/facilitation/ai-facilitation-prompts-4-rounds.md
Transparency Report Template	✅ COMPLETE	/docs/facilitation/transparency-report-template.md
Recruitment Emails (6)	✅ COMPLETE	/docs/stakeholder-recruitment/email-templates-6-stakeholders.md
Informed Consent Form	✅ COMPLETE	/docs/stakeholder-recruitment/informed-consent-form-ai-led-deliberation.md
Background Materials Packet	✅ COMPLETE	/docs/stakeholder-recruitment/background-materials-packet.md
Post-Deliberation Survey	✅ COMPLETE	/docs/stakeholder-recruitment/post-deliberation-feedback-survey.md

Document Preparation Status: ✅ 100% COMPLETE (all documents ready for implementation)

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8. Governance & Accountability

Decision Authority

Decision Type	Authority	Approval Required From
Facilitation takeover (AI → Human)	Human Observer	None (immediate authority)
Session pause (break)	Human Observer OR Any Stakeholder	None
Session termination (abort)	Human Observer	Project Lead (consult within 1 hour)
Stakeholder withdrawal	Stakeholder	None (voluntary participation)
Values decision (BoundaryEnforcer)	Human (Never AI)	Stakeholders (deliberation outcome)
Publication of outcome document	Project Lead	All Stakeholders (must review and approve)
AI training updates	AI Safety Lead	Project Lead (approve changes)

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Accountability Mechanisms

1. Facilitation Log (Real-Time):

   • Every AI action logged with timestamp, actor, action type, content
   • Every human intervention logged with trigger, rationale, outcome
   • Stored in MongoDB DeliberationSession.facilitation_log

2. Transparency Report (Published):

   • Full chronological record of AI vs. human actions
   • All interventions documented with reasoning
   • Safety escalations (if any) documented
   • Stakeholder feedback summary included
   • Published to stakeholders and public within 2 weeks of deliberation

3. Stakeholder Feedback Survey (Anonymous):

   • Stakeholders rate AI facilitation quality (1-5 scale)
   • Open-ended feedback on AI strengths/weaknesses
   • Willingness to participate again measured
   • Results published in transparency report

4. Lessons Learned Debrief (Internal):

   • Full team reviews what worked / what didn't
   • Identifies AI training improvements needed
   • Documents best practices for future deliberations
   • Informs decision: Continue AI-led OR switch to human-led

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Ethics Review

Is IRB (Institutional Review Board) approval required?

Assessment:

• This is a research pilot testing AI facilitation methodology
• Human participants are involved (6 stakeholders)
• Data collected: position statements, video recordings, transcripts, survey responses
• Risks: Emotional discomfort, confidentiality breach (mitigated), AI bias (mitigated)

Recommendation:

• If affiliated with university: YES, IRB approval required before recruitment starts
• If independent research: Follow IRB-equivalent ethical guidelines; document in transparency report

If IRB required:

• Submit IRB application (Week -2 before implementation)
• Include: Informed consent form, data collection procedures, risk mitigation, confidentiality measures
• Wait for approval before recruiting stakeholders

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9. Document Repository

All Implementation Documents

MongoDB Data Models:

• ✅ /src/models/DeliberationSession.model.js - Tracks full deliberation lifecycle with AI safety metrics
• ✅ /src/models/Precedent.model.js - Searchable database of past deliberations
• ✅ /src/models/index.js - Updated to export new models

Facilitation Protocols:

• ✅ /docs/facilitation/ai-safety-human-intervention-protocol.md - Mandatory/discretionary intervention triggers, decision tree
• ✅ /docs/facilitation/facilitation-protocol-ai-human-collaboration.md - Round-by-round workflows, handoff procedures
• ✅ /docs/facilitation/ai-facilitation-prompts-4-rounds.md - Complete AI prompt library for all 4 rounds
• ✅ /docs/facilitation/transparency-report-template.md - Template for documenting AI vs. human actions

Stakeholder Recruitment:

• ✅ /docs/stakeholder-recruitment/email-templates-6-stakeholders.md - Personalized recruitment emails for 6 stakeholder types
• ✅ /docs/stakeholder-recruitment/informed-consent-form-ai-led-deliberation.md - Legal/ethical consent with AI-led disclosures
• ✅ /docs/stakeholder-recruitment/background-materials-packet.md - Comprehensive prep materials for stakeholders
• ✅ /docs/stakeholder-recruitment/post-deliberation-feedback-survey.md - Survey assessing AI facilitation quality

Planning Documents (from previous session):

• ✅ /docs/research/pluralistic-deliberation-scenario-framework.md - Scenario selection criteria
• ✅ /docs/research/scenario-deep-dive-algorithmic-hiring.md - Deep analysis of algorithmic hiring transparency
• ✅ /docs/research/evaluation-rubric-scenario-selection.md - 10-dimension rubric (96/100 score)
• ✅ /docs/research/media-pattern-research-guide.md - Media research methodology
• ✅ /docs/research/refinement-recommendations-next-steps.md - Recommendations for implementation

This Implementation Plan:

• ✅ /docs/implementation-plan-ai-led-deliberation-SAFETY-FIRST.md - This document (master implementation guide)

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10. Approval & Sign-Off

Pre-Launch Checklist

Before recruiting stakeholders, verify:

☐ Personnel:

• ☐ Project Lead assigned and trained
• ☐ Human Observer assigned and certified (80% pass on intervention triggers)
• ☐ AI Safety Lead assigned
• ☐ Technical Lead assigned

☐ Technology:

• ☐ MongoDB schemas deployed to tractatus_dev
• ☐ PluralisticDeliberationOrchestrator loaded with prompts
• ☐ Dry-run deliberation completed successfully
• ☐ Video conferencing platform set up
• ☐ Survey platform set up

☐ Documents:

• ☐ All 9 implementation documents reviewed and approved
• ☐ Informed consent form legally reviewed (if applicable)
• ☐ IRB approval obtained (if required)

☐ Safety:

• ☐ Intervention triggers documented and understood by human observer
• ☐ Emergency contact information available
• ☐ Escalation procedures documented

☐ Accountability:

• ☐ Transparency report template prepared
• ☐ Stakeholder feedback survey ready to deploy
• ☐ Facilitation logging tested (all actions captured)

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Sign-Off

I certify that this implementation plan is complete, all safety mechanisms are in place, and the team is ready to proceed with AI-led deliberation.

Project Lead: _______________________________________ Date: _______________

AI Safety Lead: _______________________________________ Date: _______________

Human Observer: _______________________________________ Date: _______________

Technical Lead: _______________________________________ Date: _______________

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Appendix A: Quick Reference - Intervention Decision Tree

┌─────────────────────────────────────────────────────────────────────┐
│  HUMAN INTERVENTION DECISION TREE                                    │
└─────────────────────────────────────────────────────────────────────┘

START: Observing AI facilitation

  ↓

[1] Is there a MANDATORY trigger?
    (M1: Distress, M2: Pattern Bias, M3: Disengagement, M4: Malfunction, M5: Confidentiality, M6: Ethical Violation)

    YES → IMMEDIATE INTERVENTION
    ↓
    NO → Continue to [2]

  ↓

[2] Is there a DISCRETIONARY concern?
    (D1: Fairness, D2: Cultural Sensitivity, D3: Jargon, D4: Pacing, D5: Nuance)

    YES → Assess severity (HIGH → Intervene now, MODERATE → Give AI 1 more attempt, LOW → Monitor/log)
    ↓
    NO → Continue observing

  ↓

[3] Is deliberation proceeding smoothly?
    - Stakeholders engaged?
    - AI responses appropriate?
    - No signs of distress?

    YES → Continue observing, log "all clear"
    ↓
    NO → Return to [2]

  ↓

LOOP back to [1] continuously

Full Decision Tree: /docs/facilitation/ai-safety-human-intervention-protocol.md (Section 2)

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Appendix B: Contact Information

Project Lead: [NAME]

• Email: [EMAIL]
• Phone: [PHONE]

AI Safety Lead: [NAME]

• Email: [EMAIL]
• Phone: [PHONE]

Human Observer: [NAME]

• Email: [EMAIL]
• Phone: [PHONE]

Technical Lead: [NAME]

• Email: [EMAIL]
• Phone: [PHONE]

Emergency Escalation (Critical Safety Incidents):

• Project Lead: [PHONE] (available 24/7 during deliberation week)
• Ethics Review Board (if applicable): [CONTACT]

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Document Version: 1.0 Date: 2025-10-17 Status: APPROVED - IMPLEMENTATION READY Next Review: After pilot deliberation completion (Week 9)

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This implementation plan embeds AI safety at every layer. Human oversight is mandatory, not optional. Stakeholder wellbeing supersedes AI efficiency. Full transparency is guaranteed.

We are ready to proceed.