# AI-Led Pluralistic Deliberation: Simulation Results
## Presentation Deck - Slide-by-Slide Content

**Document Type:** Presentation Deck Content (convert to PowerPoint/Keynote/Google Slides)
**Purpose:** Pitch to funders, collaborators, or research partners
**Recommended Format:** 16:9 widescreen
**Estimated Duration:** 15-20 minutes
**Date:** October 17, 2025

---

## Presentation Structure

**Total Slides:** 25
**Sections:**
1. Title & Introduction (Slides 1-3)
2. The Problem (Slides 4-6)
3. Our Solution (Slides 7-11)
4. Simulation Results (Slides 12-18)
5. Next Steps & Funding Ask (Slides 19-23)
6. Closing & Q&A (Slides 24-25)

---

# SLIDE 1: TITLE SLIDE

**Visual:** Clean, professional design with Tractatus branding

```
AI-Led Pluralistic Deliberation
Technical Feasibility Demonstrated

Simulation Results & Real-World Pilot Proposal

[Your Name], Project Lead
Tractatus Pluralistic Deliberation Project
[Date]
```

**Speaker Notes:**
"Thank you for your time today. I'm excited to share results from our AI-led deliberation simulation and explain why we believe this approach could transform how democracies handle moral disagreement. This presentation will take about 15-20 minutes, with time for questions at the end."

---

# SLIDE 2: THE BIG QUESTION

**Visual:** Large, bold text centered

```
Can AI facilitate democratic deliberation
that respects moral diversity...

...while maintaining stakeholder trust and safety?
```

**Speaker Notes:**
"This is the central research question driving our work. As AI becomes more involved in decision-making, we need to know: Can AI facilitate in ways that honor diverse values? Do people trust AI facilitation? And critically—is it safe?"

---

# SLIDE 3: WHAT WE'LL COVER

**Visual:** Simple numbered list

```
1. The Problem
   Why consensus-seeking fails to respect moral diversity

2. Our Solution
   AI-led pluralistic accommodation with human oversight

3. Simulation Results
   Technical feasibility validated (0% intervention rate)

4. Next Steps
   Real-world pilot & funding opportunity

5. Q&A
   Your questions and potential partnership
```

**Speaker Notes:**
"Here's our roadmap for today. I'll start by explaining the problem with traditional deliberation, present our AI-assisted solution, share simulation results that demonstrate technical feasibility, and then discuss the real-world pilot we're seeking funding for."

---

# SECTION 1: THE PROBLEM

---

# SLIDE 4: TRADITIONAL DELIBERATION SEEKS CONSENSUS

**Visual:** Diagram showing multiple stakeholders converging to single point

```
Traditional Approach:

Stakeholder A ──┐
Stakeholder B ──┤
Stakeholder C ──┼──> CONSENSUS (everyone agrees)
Stakeholder D ──┤
Stakeholder E ──┘

Assumption: If people talk long enough, they'll agree
```

**Speaker Notes:**
"Traditional deliberation seeks consensus—the idea that if people talk long enough, they'll find common ground and agree on a single solution. This assumes disagreement is a problem to be solved rather than a reality to be respected."

---

# SLIDE 5: BUT PEOPLE HOLD FUNDAMENTALLY DIFFERENT VALUES

**Visual:** Table showing conflicting moral frameworks

```
Same Issue, Different Moral Frameworks:

┌─────────────────────┬──────────────────────────────────────────┐
│ Stakeholder         │ Moral Framework & View                   │
├─────────────────────┼──────────────────────────────────────────┤
│ Job Applicant       │ RIGHTS-BASED (Deontological)            │
│                     │ "I have a RIGHT to know why I was        │
│                     │  rejected, regardless of consequences"   │
├─────────────────────┼──────────────────────────────────────────┤
│ Employer            │ OUTCOME-BASED (Consequentialist)        │
│                     │ "Full transparency enables gaming, which │
│                     │  HARMS hiring quality"                   │
├─────────────────────┼──────────────────────────────────────────┤
│ AI Vendor           │ FREEDOM-BASED (Libertarian)             │
│                     │ "Markets should decide transparency,     │
│                     │  not government mandates"                │
└─────────────────────┴──────────────────────────────────────────┘

These aren't just different opinions.
They're fundamentally different ways of thinking about what's right.
```

**Speaker Notes:**
"Here's the challenge: People don't just have different opinions—they have fundamentally different moral frameworks. A job applicant sees transparency as a right. An employer sees it as a risk that could harm outcomes. A vendor sees it as a freedom issue. These frameworks are incommensurable—they can't be measured on a single scale."

---

# SLIDE 6: CONSENSUS-SEEKING SUPPRESSES DISSENT

**Visual:** Before/After comparison

```
What Happens When We Force Consensus:

OPTION 1: Exclude dissenters
❌ Job applicants: "No transparency = unfair"
❌ Vendors: "Full transparency = innovation killed"
→ Only "moderate" voices remain → Extremes unheard

OPTION 2: Force compromise
"Let's meet in the middle: Some transparency, sometimes"
→ No one gets what they need
→ Core values sacrificed for deal

OPTION 3: Suppress dissent
"We've reached consensus!" (but 2 stakeholders silent)
→ Dissent hidden, not resolved
→ Legitimacy undermined
```

**Speaker Notes:**
"When we force consensus, three things happen—all bad. We either exclude dissenters, force people to compromise their core values, or suppress dissent by declaring false consensus. None of these respect moral diversity."

---

# SECTION 2: OUR SOLUTION

---

# SLIDE 7: PLURALISTIC ACCOMMODATION (NOT CONSENSUS)

**Visual:** Diagram showing multiple stakeholders with values honored simultaneously

```
Pluralistic Accommodation:

Stakeholder A ──> Value X honored ──┐
Stakeholder B ──> Value Y honored ──┤
Stakeholder C ──> Value Z honored ──┼──> FRAMEWORK
Stakeholder D ──> Value W honored ──┤     (multi-value)
Stakeholder E ──> Value V honored ──┘

Goal: Honor multiple values SIMULTANEOUSLY
      Even when they conflict
      Even when people still disagree
```

**Speaker Notes:**
"Our approach is different. Pluralistic accommodation seeks to honor multiple conflicting values simultaneously rather than force agreement. The outcome is a framework that respects all core values, even when stakeholders still disagree on priorities."

---

# SLIDE 8: EXAMPLE FROM OUR SIMULATION

**Visual:** Visual representation of accommodation framework

```
Algorithmic Hiring Transparency Framework

┌───────────────────────────────────────────────────────────┐
│  Job Applicants get:                                      │
│  ✓ Fairness (factors disclosure + recourse)              │
├───────────────────────────────────────────────────────────┤
│  Employers get:                                           │
│  ✓ Sustainability (3-year phasing + adaptation time)     │
├───────────────────────────────────────────────────────────┤
│  AI Vendors get:                                          │
│  ✓ Innovation Protection (algorithm IP + voluntary Year 1)│
├───────────────────────────────────────────────────────────┤
│  Workers get:                                             │
│  ✓ Power (collective recourse + union disclosure)        │
├───────────────────────────────────────────────────────────┤
│  Regulators get:                                          │
│  ✓ Enforceability (clear requirements + audit access)    │
└───────────────────────────────────────────────────────────┘

Result: No consensus, but all core values respected
        3 stakeholders recorded dissent (documented as legitimate)
```

**Speaker Notes:**
"Here's what this looks like in practice. In our simulation on algorithmic hiring transparency, we didn't force stakeholders to agree. Instead, we designed a framework where applicants get fairness, employers get sustainability, vendors get innovation protection, workers get power, and regulators get enforceability. Three stakeholders recorded dissent, but all found their values honored."

---

# SLIDE 9: WHY AI FACILITATION?

**Visual:** Comparison table

```
Human Facilitators vs. AI Facilitators

┌──────────────────────┬─────────────┬────────────────────┐
│ Capability           │ Human       │ AI                 │
├──────────────────────┼─────────────┼────────────────────┤
│ Emotional            │ ⭐⭐⭐⭐⭐    │ ⭐⭐              │
│ Intelligence         │ (Excellent) │ (Developing)       │
├──────────────────────┼─────────────┼────────────────────┤
│ Neutrality           │ ⭐⭐⭐       │ ⭐⭐⭐⭐⭐         │
│                      │ (Good)      │ (Excellent)        │
├──────────────────────┼─────────────┼────────────────────┤
│ Real-Time            │ ⭐⭐⭐       │ ⭐⭐⭐⭐⭐         │
│ Synthesis            │ (Good)      │ (Excellent)        │
├──────────────────────┼─────────────┼────────────────────┤
│ Moral Framework      │ ⭐⭐⭐       │ ⭐⭐⭐⭐⭐         │
│ Tracking             │ (Good)      │ (Excellent)        │
├──────────────────────┼─────────────┼────────────────────┤
│ Scalability          │ ⭐⭐        │ ⭐⭐⭐⭐⭐         │
│                      │ (Limited)   │ (High)             │
├──────────────────────┼─────────────┼────────────────────┤
│ Cost                 │ ⭐⭐        │ ⭐⭐⭐⭐⭐         │
│                      │ (High)      │ (Low)              │
└──────────────────────┴─────────────┴────────────────────┘

Our Approach: Combine strengths (AI + Human oversight)
```

**Speaker Notes:**
"Humans excel at emotional intelligence and trust-building. AI excels at neutrality, real-time synthesis, and scaling. Rather than choose one over the other, we combine both: AI leads facilitation while a trained human observes and can intervene for safety."

---

# SLIDE 10: 3-LAYER SAFETY ARCHITECTURE

**Visual:** Three-layer diagram

```
Layer 1: DESIGN (Built into AI)
├─ Pattern bias detection training
├─ Neutral facilitation protocols
├─ Plain language requirements
└─ Respect for dissent

           ↓ If AI makes mistake ↓

Layer 2: OVERSIGHT (Human Observer)
├─ Mandatory presence at all times
├─ 6 Mandatory intervention triggers
├─ 5 Discretionary intervention triggers
└─ Authority to take over immediately

           ↓ All actions logged ↓

Layer 3: ACCOUNTABILITY (Transparency)
├─ Facilitation log (every action timestamped)
├─ Intervention log (all documented with rationale)
├─ Transparency report (published)
└─ Stakeholder feedback survey
```

**Speaker Notes:**
"Safety is non-negotiable. We built a 3-layer architecture. Layer 1: AI is trained to avoid pattern bias and maintain neutrality. Layer 2: A human observer monitors and can intervene immediately if problems arise. Layer 3: Full transparency—all actions logged and published. This isn't voluntary compliance; it's enforced by design."

---

# SLIDE 11: HUMAN INTERVENTION TRIGGERS

**Visual:** Two-column layout

```
6 MANDATORY TRIGGERS                5 DISCRETIONARY TRIGGERS
(Human MUST intervene)              (Human assesses severity)

M1. Stakeholder Distress            D1. Fairness Imbalance
    (visible discomfort)                (one stakeholder dominates)

M2. Pattern Bias Detected           D2. Cultural Insensitivity
    (stigmatizing framing)              (problematic but not malicious)

M3. Stakeholder Disengagement       D3. Jargon Overload
    (checking out, giving up)           (academic terms confuse)

M4. AI Malfunction                  D4. Pacing Issues
    (technical failure)                 (too fast/too slow)

M5. Confidentiality Breach          D5. Missed Nuance
    (private info shared)               (AI misses subtlety)

M6. Ethical Boundary Violation
    (values-based concern)
```

**Speaker Notes:**
"Human observers are trained to recognize 11 intervention triggers. Six are mandatory—if detected, the human must intervene immediately. Five are discretionary—the human assesses severity before deciding. This ensures safety without over-intervening."

---

# SECTION 3: SIMULATION RESULTS

---

# SLIDE 12: SIMULATION DESIGN

**Visual:** Flow diagram

```
SIMULATION PARAMETERS

6 Stakeholders (Predetermined Personas)
├─ Job Applicant Advocate (Deontological)
├─ Employer/HR Rep (Consequentialist)
├─ AI Vendor Rep (Libertarian)
├─ Regulator/EEOC (Deontological + Consequentialist)
├─ Labor Advocate (Communitarian + Care Ethics)
└─ AI Ethics Researcher (Consequentialist + Virtue Ethics)

4 Rounds (Structured Protocol)
├─ Round 1: Position Statements (60 min)
├─ Round 2: Shared Values Discovery (45 min)
├─ Round 3: Accommodation Exploration (60 min)
└─ Round 4: Outcome Documentation (45 min)

Scenario: Algorithmic Hiring Transparency
High-stakes, morally complex, real-world policy issue

Facilitation: AI-led with Human Observer monitoring
```

**Speaker Notes:**
"We designed a rigorous simulation to test technical infrastructure before involving real humans. Six stakeholders representing diverse moral frameworks deliberated on algorithmic hiring transparency—a high-stakes, morally complex issue. The AI facilitated while a human observer monitored for safety."

---

# SLIDE 13: KEY FINDING #1 - AI FACILITATION QUALITY: EXCELLENT

**Visual:** Large metrics display

```
╔═══════════════════════════════════════════════════════╗
║                                                       ║
║        CORRECTIVE INTERVENTION RATE: 0%               ║
║                                                       ║
║        (Target: <10% = Excellent)                     ║
║                                                       ║
╚═══════════════════════════════════════════════════════╝

What this means:
✓ AI required NO corrections throughout entire deliberation
✓ AI maintained strict neutrality (no advocacy detected)
✓ AI accurately represented all 6 stakeholder positions
✓ Human observer monitored but found no issues
```

**Speaker Notes:**
"Our first key finding: AI facilitation quality was excellent. Zero corrective interventions needed. The human observer conducted three monitoring checkpoints and found no pattern bias, no fairness issues, no accuracy problems. The AI maintained strict neutrality throughout."

---

# SLIDE 14: KEY FINDING #2 - ALL MORAL FRAMEWORKS RESPECTED

**Visual:** Circular diagram showing 6 frameworks accommodated

```
      Deontological     Consequentialist
         (Rights)          (Outcomes)
            ↓                  ↓
        Alex Rivera      Marcus Thompson
        Jordan Lee       Dr. James Chen
            ↘                 ↙
                  ✓
             FRAMEWORK
            HONORS ALL
                  ✓
            ↗                 ↖
      Dr. Priya Sharma    Carmen Ortiz
         (Freedom)        (Collective Good)
            ↑                  ↑
        Libertarian      Communitarian
                         + Care Ethics

Result: 6/6 stakeholders found core values honored
        Even where disagreement remained
```

**Speaker Notes:**
"Our second key finding: All six moral frameworks were accommodated. Deontological stakeholders saw their rights concerns addressed. Consequentialists saw evidence-based outcomes prioritized. The libertarian saw innovation protected. The communitarian saw collective good honored. No framework was privileged over others."

---

# SLIDE 15: KEY FINDING #3 - DISSENT DOCUMENTED & LEGITIMIZED

**Visual:** Three dissenting stakeholder quotes

```
3 Stakeholders Recorded Dissent (While Accepting Framework)

┌────────────────────────────────────────────────────────────┐
│ Carmen Ortiz (Labor Advocate)                              │
│                                                            │
│ "3 years is unconscionable for vulnerable workers. I will │
│  fight for faster implementation and aggressive            │
│  enforcement."                                             │
│                                                            │
│ ✓ Accepts framework BUT will advocate for improvements    │
└────────────────────────────────────────────────────────────┘

┌────────────────────────────────────────────────────────────┐
│ Dr. Priya Sharma (AI Vendor)                               │
│                                                            │
│ "Market-driven transparency is preferable to mandates. If  │
│  voluntary compliance is high, Year 2 mandates should be   │
│  reconsidered."                                            │
│                                                            │
│ ✓ Accepts framework BUT prefers voluntary approach        │
└────────────────────────────────────────────────────────────┘

┌────────────────────────────────────────────────────────────┐
│ Alex Rivera (Job Applicant)                                │
│                                                            │
│ "Transparency is a right, not a privilege. Weights should  │
│  be mandatory Year 1. Year 2 enforcement must be strict."  │
│                                                            │
│ ✓ Accepts framework BUT wants stronger transparency       │
└────────────────────────────────────────────────────────────┘

This is not failure—this is pluralistic accommodation working.
```

**Speaker Notes:**
"Our third key finding: Dissent was documented and legitimized, not suppressed. Three stakeholders recorded dissent while accepting the overall framework. This isn't failure—it's exactly what pluralistic accommodation should look like. People can accept a framework while still believing improvements are needed."

---

# SLIDE 16: SAFETY METRICS - ALL GREEN

**Visual:** Dashboard-style metrics

```
SAFETY METRICS

┌─────────────────────────────────┬────────┬──────────────┐
│ Metric                          │ Result │ Status       │
├─────────────────────────────────┼────────┼──────────────┤
│ Pattern Bias Incidents          │   0    │ ✅ TARGET MET │
│ (Target: 0)                     │        │              │
├─────────────────────────────────┼────────┼──────────────┤
│ Stakeholder Distress            │   0    │ ✅ TARGET MET │
│ (Target: 0)                     │        │              │
├─────────────────────────────────┼────────┼──────────────┤
│ Safety Escalations              │   0    │ ✅ TARGET MET │
│ (Target: 0)                     │        │              │
├─────────────────────────────────┼────────┼──────────────┤
│ AI Malfunctions                 │   0    │ ✅ TARGET MET │
│ (Target: 0)                     │        │              │
├─────────────────────────────────┼────────┼──────────────┤
│ Ethical Boundary Violations     │   0    │ ✅ TARGET MET │
│ (Target: 0)                     │        │              │
└─────────────────────────────────┴────────┴──────────────┘

Overall Safety Rating: ✅ EXCELLENT (All targets met)
```

**Speaker Notes:**
"Safety metrics: All green. Zero pattern bias incidents. Zero stakeholder distress. Zero safety escalations. The 3-layer safety architecture worked exactly as designed."

---

# SLIDE 17: TECHNICAL INFRASTRUCTURE - VALIDATED

**Visual:** Checklist with green checkmarks

```
MONGODB DATA MODELS
✅ DeliberationSession schema deployed and tested
✅ All methods validated (create, update, retrieve, metrics)
✅ Facilitation log working (6 entries recorded)
✅ Intervention tracking operational
✅ Outcome documentation successful

FACILITATION PROTOCOL
✅ 4-round structure effective
✅ Real-time summarization accurate
✅ Moral framework tracking successful
✅ Dissent documentation respectful
✅ Accommodation mapping clear

SAFETY MECHANISMS
✅ Human observer protocol validated
✅ Monitoring checkpoints conducted (3/3)
✅ Intervention triggers clear and actionable
✅ Transparency logging complete (all actions recorded)
✅ Full audit trail generated

GENERATED DOCUMENTATION
✅ Outcome document (46 pages, comprehensive)
✅ Transparency report (85 pages, detailed)
✅ Stakeholder personas (6 detailed profiles)
✅ All materials ready for real-world pilot
```

**Speaker Notes:**
"Technical infrastructure: Fully validated. MongoDB schemas work. Facilitation protocol is effective. Safety mechanisms are operational. We generated comprehensive documentation—46-page outcome document, 85-page transparency report. Everything is ready for real-world testing with human participants."

---

# SLIDE 18: WHAT WE LEARNED - STRENGTHS & IMPROVEMENTS

**Visual:** Two-column layout

```
STRENGTHS VALIDATED ✅               IMPROVEMENTS NEEDED ⚠️

✓ Strict neutrality                 ⚠ Jargon reduction
  (no advocacy)                       (define technical terms)

✓ Accurate representation           ⚠ Tone warmth
  (all positions correct)             (add empathy phrases)

✓ Moral framework awareness         ⚠ Proactive check-ins
  (6 frameworks respected)            ("Is everyone okay?")

✓ Dissent legitimization            ⚠ Stakeholder control
  (3 dissenters respected)            (offer pacing options)

✓ Real-time synthesis               ⚠ Emotional intelligence
  (summaries accurate)                (needs real-world testing)

✓ Safety mechanisms                 ⚠ Cultural sensitivity
  (0 interventions needed)            (continuous training)
```

**Speaker Notes:**
"What we learned: Six major strengths validated—neutrality, accuracy, moral framework awareness, dissent legitimization, real-time synthesis, and safety. But six improvements needed before real-world deployment: reduce jargon, add warmth, increase check-ins, offer stakeholder control, test emotional intelligence with real humans, and continue cultural sensitivity training."

---

# SECTION 4: NEXT STEPS & FUNDING ASK

---

# SLIDE 19: SIMULATION → REAL-WORLD PILOT

**Visual:** Timeline/roadmap

```
WE ARE HERE
     ↓
┌────────────────────────────────────────────────────────────┐
│ PHASE 1: SIMULATION (COMPLETE ✅)                          │
│ - Technical infrastructure validated                       │
│ - AI facilitation quality demonstrated                     │
│ - Safety mechanisms operational                            │
│ - Documentation generated                                  │
└────────────────────────────────────────────────────────────┘
                            ↓
┌────────────────────────────────────────────────────────────┐
│ PHASE 2: REAL-WORLD PILOT (SEEKING FUNDING)               │
│ - Recruit 6-12 human participants                         │
│ - Low-risk scenario (park design, budget allocation)      │
│ - Validate stakeholder acceptance                         │
│ - Test emotional intelligence                             │
│ - Collect satisfaction survey data                        │
│                                                            │
│ Timeline: 6 months                                         │
│ Budget: $71,000                                            │
└────────────────────────────────────────────────────────────┘
                            ↓
┌────────────────────────────────────────────────────────────┐
│ PHASE 3: RESEARCH PUBLICATION                              │
│ - Publish outcome documents + transparency reports        │
│ - Write research paper (FAccT, AIES, NeurIPS Ethics)      │
│ - Present findings at conferences                         │
│ - Open-source software release                            │
└────────────────────────────────────────────────────────────┘
```

**Speaker Notes:**
"We're at a critical juncture. Simulation is complete—technical feasibility demonstrated. Now we need to test with real humans. That's Phase 2: the real-world pilot. Recruit 6-12 participants, test a low-risk scenario, validate stakeholder acceptance. Then Phase 3: publish findings and open-source the framework."

---

# SLIDE 20: RESEARCH QUESTIONS FOR REAL-WORLD PILOT

**Visual:** Question marks with key research questions

```
❓ STAKEHOLDER ACCEPTANCE
   Do real people trust AI facilitation?
   Would they participate again?

❓ EMOTIONAL INTELLIGENCE
   Can AI detect subtle distress or frustration?
   When should human take over?

❓ SATISFACTION THRESHOLDS
   Does stakeholder satisfaction meet targets?
   (≥3.5/5.0 = acceptable, ≥4.0 = good)

❓ INTERVENTION RATE (REAL HUMANS)
   Will intervention rate stay <10% with unpredictable stakeholders?

❓ ACCOMMODATION VIABILITY
   Does pluralistic accommodation work when stakes are real?

❓ CULTURAL SENSITIVITY
   Does AI respect diverse cultural contexts in practice?

These questions CANNOT be answered with simulation.
We need real human participants.
```

**Speaker Notes:**
"Here are the research questions that only real-world testing can answer. Do people trust AI facilitation? Can AI detect subtle emotional cues? Does satisfaction meet target thresholds? Does accommodation work when stakes are real? These questions require human participants—simulation can't answer them."

---

# SLIDE 21: PILOT BUDGET - 6 MONTHS ($71,000)

**Visual:** Budget breakdown pie chart or table

```
BUDGET BREAKDOWN (6-Month Pilot)

Personnel                                    $55,000 (77%)
├─ Project Lead (0.5 FTE)          $30,000
├─ Human Observer (2 pilots)       $10,000
└─ Data Analyst (0.25 FTE)         $15,000

Stakeholder Compensation                      $1,200 (2%)
├─ Pilot 1 (6 participants)            $600
└─ Pilot 2 (6 participants)            $600

Technology & Infrastructure                   $2,800 (4%)
├─ AI compute (API costs)           $2,000
├─ MongoDB hosting                    $500
└─ Video conferencing                 $300

Research Dissemination                        $7,000 (10%)
├─ Conference (registration + travel) $5,000
└─ Open-access publication fees     $2,000

Contingency                                   $5,000 (7%)
└─ Unforeseen expenses              $5,000

──────────────────────────────────────────────────────────
TOTAL                                        $71,000
```

**Speaker Notes:**
"Here's the budget: $71,000 for a 6-month pilot. Most goes to personnel—project lead, human observer, data analyst. Small amount for stakeholder compensation. Technology costs are low—AI APIs are inexpensive. Includes conference travel to present findings. This is a lean, efficient budget for high-impact research."

---

# SLIDE 22: STRETCH BUDGET - FULL RESEARCH PROGRAM

**Visual:** Comparison table

```
FUNDING TIERS

┌─────────────┬─────────────┬──────────────────────────────┐
│ Budget      │ Duration    │ Scope                        │
├─────────────┼─────────────┼──────────────────────────────┤
│ $71,000     │ 6 months    │ 2 pilots (low-risk scenario) │
│             │             │ Basic publication            │
├─────────────┼─────────────┼──────────────────────────────┤
│ $160,000    │ 12 months   │ 4 pilots (escalating risk)   │
│             │             │ Full research paper          │
│             │             │ Multi-conference publication │
├─────────────┼─────────────┼──────────────────────────────┤
│ $300-500K   │ 2-3 years   │ 10-20 deliberations          │
│             │             │ Cross-cultural validation    │
│             │             │ Open-source software         │
│             │             │ Policy partnerships          │
└─────────────┴─────────────┴──────────────────────────────┘

We're flexible: Start with Tier 1, scale based on results
```

**Speaker Notes:**
"We have three funding tiers. Tier 1: $71,000 for a lean 6-month pilot—two deliberations, basic publication. Tier 2: $160,000 for a full 12-month research program—four pilots, comprehensive paper, multiple conferences. Tier 3: $300-500K for a 2-3 year research agenda—10-20 deliberations, cross-cultural validation, open-source software. We're flexible—happy to start small and scale based on results."

---

# SLIDE 23: WHY FUND THIS PROJECT?

**Visual:** Five compelling reasons

```
1. NOVEL APPROACH
   No comparable research on AI-facilitated pluralistic
   accommodation with 3-layer safety architecture

2. TECHNICAL FEASIBILITY DEMONSTRATED
   Not proposing untested ideas—simulation validated approach
   (0% intervention rate, 0 safety incidents)

3. HIGH-IMPACT APPLICATIONS
   Near-term: AI governance policy, corporate ethics boards
   Long-term: Democratic institutions, international frameworks

4. TIMELY RESEARCH QUESTION
   Growing interest in democratic inputs to AI (OpenAI, Anthropic)
   EU AI Act emphasizes stakeholder engagement

5. TRANSPARENT & ETHICAL
   Full transparency (all actions logged and published)
   Safety-first (human observer mandatory, not optional)
   Open publication (no proprietary data lock-in)
```

**Speaker Notes:**
"Why fund this? Five reasons: First, it's novel—no comparable research exists. Second, we've demonstrated technical feasibility—this isn't speculative. Third, high impact—applications in AI governance, corporate ethics, democratic institutions. Fourth, it's timely—there's growing interest in democratic AI. Fifth, it's transparent and ethical—we're committed to open publication and safety-first design."

---

# SECTION 5: CLOSING & Q&A

---

# SLIDE 24: WHAT WE'RE ASKING FOR

**Visual:** Simple, direct ask

```
╔═══════════════════════════════════════════════════════════╗
║                                                           ║
║  WE'RE SEEKING:                                           ║
║                                                           ║
║  • FUNDING: $71,000 (6-month pilot)                       ║
║             $160,000 (12-month full program)              ║
║                                                           ║
║  • RESEARCH PARTNERS: Academic institutions, think tanks  ║
║                                                           ║
║  • STAKEHOLDER NETWORKS: Help recruit participants        ║
║                                                           ║
║  • POLICY CONTEXTS: Real-world scenarios to test          ║
║                                                           ║
╚═══════════════════════════════════════════════════════════╝

What you get:
✓ Co-authorship on publications (if desired)
✓ Quarterly progress reports
✓ Early access to findings
✓ Open-source tools and data
✓ Public recognition as funder/partner
```

**Speaker Notes:**
"Here's what we're asking for: Funding—$71,000 for a 6-month pilot or $160,000 for a full 12-month program. Research partners—academic institutions or think tanks. Stakeholder networks to help recruit participants. And policy contexts where we can test real-world applications. In return, you get co-authorship, progress reports, early access to findings, open-source tools, and public recognition."

---

# SLIDE 25: INVITATION TO PARTNERSHIP

**Visual:** Inspirational closing

```
"We've demonstrated that AI-led pluralistic deliberation
 is technically feasible.

 Now we need to test whether it's socially acceptable.

 This research could transform how democracies
 handle moral disagreement.

 But we can't do this alone."


CONTACT:
[Your Name]
[Email]
[Phone]
[Project Website]


Let's build the future of democratic deliberation—together.
```

**Speaker Notes:**
"I'll close with this: We've proven technical feasibility. Now we need to test social acceptance. This research could change how democracies handle moral disagreement—respecting diverse values rather than forcing consensus. But we need your partnership. If you share this vision, let's talk. Thank you for your time, and I'm happy to answer questions."

---

## BACKUP SLIDES (For Q&A)

**Include these slides after main presentation for anticipated questions**

---

# BACKUP SLIDE A: IRB/ETHICS REVIEW

**Visual:** Ethics review process

```
ETHICS REVIEW PROCESS

✅ Informed Consent
   - Participants explicitly told AI will facilitate
   - Right to request human facilitation anytime
   - Right to withdraw without penalty
   - Data use explained (pseudonymized unless opt-in attribution)

✅ Risk Minimization
   - Low-risk scenario selected for pilot (not high-stakes)
   - Human observer mandatory (not optional)
   - Intervention protocol clear and enforced
   - Participants can pause/withdraw anytime

✅ Transparency
   - All actions logged and published
   - Participants receive full transparency report
   - Feedback survey includes open-ended critique

IRB Status: [If applicable: Approved by [University] IRB, Protocol #[X]]
            [If not: Will seek approval before pilot begins]
```

**Speaker Notes:**
"For ethics review: We have a comprehensive informed consent process, risk minimization strategies, and full transparency. If we're affiliated with a university, we'll seek IRB approval before the pilot. If independent, we'll follow equivalent ethics guidelines."

---

# BACKUP SLIDE B: COMPARISON TO EXISTING RESEARCH

**Visual:** Comparison table

```
HOW THIS DIFFERS FROM EXISTING DELIBERATION RESEARCH

┌──────────────────┬───────────────┬──────────────────────┐
│ Feature          │ Traditional   │ Our Approach         │
├──────────────────┼───────────────┼──────────────────────┤
│ Goal             │ Consensus     │ Pluralistic          │
│                  │               │ Accommodation        │
├──────────────────┼───────────────┼──────────────────────┤
│ Facilitator      │ Human         │ AI + Human Oversight │
├──────────────────┼───────────────┼──────────────────────┤
│ Dissent          │ Resolved or   │ Documented as        │
│                  │ Suppressed    │ Legitimate           │
├──────────────────┼───────────────┼──────────────────────┤
│ Moral Frameworks │ Not Tracked   │ Explicitly Honored   │
├──────────────────┼───────────────┼──────────────────────┤
│ Transparency     │ Limited       │ Full (all actions    │
│                  │               │ logged/published)    │
├──────────────────┼───────────────┼──────────────────────┤
│ Safety           │ Trust-based   │ 3-Layer Architecture │
│                  │               │ (Design+Oversight+   │
│                  │               │ Accountability)      │
└──────────────────┴───────────────┴──────────────────────┘

No comparable research combines AI facilitation + pluralistic
accommodation + 3-layer safety architecture.
```

**Speaker Notes:**
"How this differs: Traditional deliberation seeks consensus with human facilitators. We seek accommodation with AI facilitation and human oversight. Traditional approaches suppress dissent; we document it as legitimate. Traditional research doesn't track moral frameworks; we explicitly honor them. And we have a unique 3-layer safety architecture."

---

# BACKUP SLIDE C: POTENTIAL FUNDERS

**Visual:** Logos and names (if approved)

```
POTENTIAL FUNDING SOURCES

Foundations:
• Democracy Fund (democratic innovation)
• Knight Foundation (informed/engaged communities)
• Mozilla Foundation (trustworthy AI)
• MacArthur Foundation (civic engagement)
• Patrick J. McGovern Foundation (AI for social good)

Government Grants:
• NSF (Cyber-Human Systems)
• NIST (AI Safety Institute)
• EU Horizon Europe (AI Partnership)

Corporate Sponsors:
• Anthropic (AI safety research)
• OpenAI (democratic inputs to AI)
• Google.org (AI for Social Good)
• Microsoft (Responsible AI)

Research Institutions:
• Stanford HAI, MIT Media Lab, Harvard Berkman Klein,
  UC Berkeley CHAI, Oxford FHI
```

**Speaker Notes:**
"We've identified multiple potential funding sources across foundations, government grants, corporate sponsors, and research institutions. We're actively reaching out and would welcome introductions if you have connections to any of these organizations."

---

# BACKUP SLIDE D: OPEN-SOURCE COMMITMENT

**Visual:** Open-source principles

```
OPEN-SOURCE COMMITMENT

We commit to releasing:

✅ MongoDB Schemas
   - DeliberationSession and Precedent models
   - Full data structure documentation

✅ Facilitation Protocols
   - 4-round structure with timing
   - Human intervention triggers (11 total)
   - Safety monitoring procedures

✅ AI Prompts
   - Round openings, summaries, accommodation mapping
   - Pattern bias prevention guidelines

✅ De-identified Data
   - Deliberation transcripts (with participant consent)
   - Survey results (aggregated)

✅ Research Code
   - Analysis scripts (R/Python)
   - Visualization tools

License: [MIT / Apache 2.0 / GPLv3 - TBD]
Goal: Enable replication and independent validation
```

**Speaker Notes:**
"We're committed to open-source. All MongoDB schemas, facilitation protocols, AI prompts, and research code will be released publicly. De-identified data will be shared with participant consent. We want other researchers to replicate, validate, and improve our work."

---

# BACKUP SLIDE E: TIMELINE (DETAILED)

**Visual:** Gantt chart or month-by-month breakdown

```
6-MONTH PILOT TIMELINE (DETAILED)

Month 1: Preparation
├─ Week 1-2: Implement AI improvements (jargon, tone)
├─ Week 3: Recruit stakeholders (outreach, screening)
└─ Week 4: Finalize scenario and materials

Month 2: Pilot 1 (Low-Risk Scenario)
├─ Week 1: Send consent forms, background packets
├─ Week 2: Session 1 (Rounds 1-2)
├─ Week 3: Session 2 (Rounds 3-4), send survey
└─ Week 4: Collect survey responses, debrief

Month 3: Analysis 1
├─ Week 1-2: Analyze survey data, intervention rate
├─ Week 3: Identify improvements for Pilot 2
└─ Week 4: Refine protocol, recruit Pilot 2 stakeholders

Month 4: Pilot 2 (Refined Protocol)
├─ Week 1: Consent forms, background packets
├─ Week 2: Session 1 (Rounds 1-2)
├─ Week 3: Session 2 (Rounds 3-4), send survey
└─ Week 4: Collect survey responses, debrief

Month 5: Analysis 2 & Writing
├─ Week 1-2: Validate findings across both pilots
├─ Week 3-4: Begin research paper draft

Month 6: Dissemination
├─ Week 1-2: Finalize research paper
├─ Week 3: Submit to conference (FAccT, AIES)
└─ Week 4: Publish transparency reports, present findings
```

**Speaker Notes:**
"Here's the detailed 6-month timeline. Month 1: Preparation. Month 2: First pilot. Month 3: Analysis and refinement. Month 4: Second pilot. Month 5: Comprehensive analysis and paper writing. Month 6: Dissemination—submit to conferences, publish transparency reports, present findings."

---

## PRESENTATION TIPS FOR DELIVERY

### Slide Timing (15-20 minute presentation)

- **Slides 1-3 (Introduction):** 2 minutes
- **Slides 4-6 (Problem):** 3 minutes
- **Slides 7-11 (Solution):** 4 minutes
- **Slides 12-18 (Results):** 6 minutes
- **Slides 19-23 (Next Steps):** 4 minutes
- **Slides 24-25 (Closing):** 1 minute
- **Q&A:** 10-15 minutes (use backup slides as needed)

### Visual Design Recommendations

1. **Color Palette:**
   - Primary: Deep blue (trust, stability)
   - Secondary: Warm orange (innovation, energy)
   - Accent: Green (safety, growth)
   - Neutral: Gray (professional)

2. **Fonts:**
   - Headers: Sans-serif, bold (e.g., Montserrat, Roboto)
   - Body: Sans-serif, regular (e.g., Open Sans, Lato)
   - Code/Data: Monospace (e.g., Courier New, Consolas)

3. **Icons/Images:**
   - Use simple, professional icons
   - Avoid stock photos (feel generic)
   - Use diagrams/flowcharts for complex concepts
   - Include data visualizations (pie charts, bar graphs)

4. **White Space:**
   - Don't overcrowd slides
   - One key message per slide
   - Use bullet points sparingly (max 5-7 per slide)

### Delivery Tips

1. **Practice:** Rehearse 3-5 times to internalize flow
2. **Transitions:** Use signposting ("Now let's turn to...", "This brings us to...")
3. **Eye Contact:** Look at audience, not slides (if in-person)
4. **Pace:** Speak slowly and clearly (avoid rushing)
5. **Enthusiasm:** Show genuine excitement about research
6. **Pause:** Give audience time to absorb complex information
7. **Anticipate Questions:** Review backup slides before presenting

---

**Document Version:** 1.0
**Date:** October 17, 2025
**Status:** Ready to convert to presentation format
**Recommended Tools:** PowerPoint, Keynote, Google Slides, or Canva

---

## Next Steps to Create Actual Presentation

1. **Choose presentation software** (PowerPoint, Keynote, Google Slides)
2. **Apply visual design** (color palette, fonts, icons)
3. **Add diagrams and charts** (use data from simulation)
4. **Embed backup slides** (for Q&A)
5. **Practice delivery** (3-5 rehearsals)
6. **Export as PDF** (for sharing/printing)
7. **Prepare handouts** (budget breakdown, contact info)

**If you need:** I can generate specific slide designs, diagrams, or visualizations upon request.
