tractatus/docs/research/pluralistic-deliberation-scenario-framework.md

Strategic Framework for Pluralistic Deliberation Scenario Selection

Project: Tractatus Framework - PluralisticDeliberationOrchestrator Document Type: Research Foundation Status: Complete - Ready for Implementation Created: 2025-10-17 Author: John Stroh (with Claude analysis) Version: 1.0

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Executive Summary

This document establishes a rigorous, theory-grounded framework for selecting demonstration scenarios for the PluralisticDeliberationOrchestrator component of the Tractatus Framework.

Purpose

The PluralisticDeliberationOrchestrator aims to facilitate multi-stakeholder deliberation across competing moral frameworks in a non-hierarchical manner. To demonstrate this capability effectively, we need scenarios that:

1. Showcase pluralistic reasoning (multiple legitimate moral frameworks in tension)
2. Avoid pattern bias (no vulnerable groups unnecessarily centered, low vicarious harm risk)
3. Demonstrate practical value (relevant to real-world governance challenges)
4. Teach generalizable principles (applicable beyond specific case)
5. Engage audiences (media-salient, timely, comprehensible)

Key Findings

Dimensional Analysis Reveals:

• Conflict type matters more than scale (small-scale can demonstrate principles effectively)
• Resource allocation and procedural conflicts are safer than identity conflicts for initial demonstrations
• Emerging issues (before polarization hardens) offer best demonstration opportunities
• Economic/technical conflicts lower risk than ideological/cultural conflicts

Top Recommendations:

Tier 1 Scenarios (Strong Candidates for MVP):

1. Algorithmic Hiring Transparency - Multiple frameworks, timely, not identity-based
2. Remote Work Location Pay - Economic focus, geographic not identity dimension
3. Public Park Usage Priority - Local, concrete, low-stakes, shows negotiation
4. Open-Source Licensing - Ideological but not partisan, technical community
5. University Lecture Recording - Multi-sided, educational context, clear trade-offs

Recommended Starting Point:

• Primary: Algorithmic Hiring Transparency
• Secondary: Remote Work Location Pay
• Rationale: Both are timely, affect broad audiences, demonstrate multiple moral frameworks without centering vulnerable identities

Document Structure

This framework consists of:

1. Four-Dimensional Analysis of conflict types and structures
2. Pattern Bias Risk Assessment to ensure ethical demonstration practices
3. Scenario Taxonomy (Tier 1-3 classifications with rationale)
4. Media Interest Analysis to identify timely, salient issues
5. Selection Methodology for systematic scenario evaluation

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Table of Contents

1. Methodological Foundation
2. Dimension 1: Scale & Stakeholder Structure
3. Dimension 2: Conflict Type Taxonomy
4. Dimension 3: Differentiating Attributes & Pattern Bias Risk
5. Dimension 4: Media Interest Patterns
6. Scenario Taxonomy (Tiers 1-3)
7. Strategic Selection Criteria
8. Recommendations
9. Next Steps

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1. Methodological Foundation

Why Systematic Scenario Selection Matters

The Challenge: Initial planning focused on mental health crisis scenarios (privacy vs. safety). While theoretically sound, this raises ethical concerns:

• Vicarious harm: Users might see themselves in crisis scenarios
• Re-traumatization: Mental health topics can trigger distress
• Vulnerable populations: Centering crisis situations risks exploitation

The Solution: A theory-driven, systematic approach to scenario selection that:

1. Starts with theory (conflict types, moral frameworks)
2. Maps to concrete examples (specific scenarios)
3. Evaluates systematically (rubric-based scoring)
4. Prioritizes safety (pattern bias risk assessment)
5. Considers strategy (media patterns, demonstration value)

Philosophical Foundations

Foundational Pluralism (from v2 plan):

• Multiple irreducibly distinct moral frameworks exist
• No supervalue subsumes all others (deontology ≠ consequentialism ≠ virtue ethics)
• Value conflicts are features, not bugs
• Rational regret is possible even when right choice made

Implications for Scenario Selection:

• Scenarios must demonstrate genuine incommensurability (not just preference differences)
• Must show multiple legitimate frameworks (not "right vs. wrong")
• Should enable non-hierarchical resolution (accommodation, not domination)
• Must document moral remainder (what's lost in any choice)

Design Principles

1. Theory-Driven

• Start with conflict taxonomy (types of moral tensions)
• Map to stakeholder structures (who's involved?)
• Identify frameworks in tension (which moral reasoning applies?)

2. Safety-First

• Avoid scenarios centering vulnerable populations
• Minimize vicarious harm risk
• No re-traumatization potential
• Respect that demonstrations have real-world impact

3. Demonstrability

• General audiences must grasp scenario quickly (<5 minutes)
• Stakeholders clearly identifiable
• Moral frameworks recognizable
• Outcomes visualizable

4. Generalizability

• Specific scenarios teach broader principles
• Applicable to other contexts
• Show deliberation process, not just outcomes

5. Strategic

• Consider media patterns (current salience)
• Avoid already-polarized issues (entrenched positions hard to deliberate)
• Emerging issues offer better opportunities

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2. Dimension 1: Scale & Stakeholder Structure

Scale Taxonomy

Conflicts occur at different scales, each with distinct deliberation dynamics:

INDIVIDUAL ←→ SMALL GROUP ←→ LARGE GROUP ←→ SOCIETAL ←→ GLOBAL

Detailed Scale Analysis

Individual vs. Individual

Characteristics:

• Dyadic relationship
• Direct personal stakes
• Often relational/interpersonal
• Low systemic impact

Examples:

• Privacy of correspondence (friend reads another's diary)
• Contract dispute (roommate agreement breach)
• Property boundary (fence line dispute)

Deliberation Dynamics:

• Mediation model
• Focus on relationship preservation
• Personal trust matters

Demonstration Value: LOW

• Too small-scale for governance demonstration
• Difficult to generalize principles
• May feel trivial to audiences

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Individual vs. Small Group (5-20 people)

Characteristics:

• Power asymmetry (one vs. many)
• Individual rights vs. group cohesion
• Accommodation vs. disruption trade-off

Examples:

• Employee requests religious accommodation (affects team workflow)
• Student objects to group project assignment (fairness vs. pedagogy)
• Homeowner opposes neighborhood association rule

Deliberation Dynamics:

• Individual rights protection essential
• Group efficiency matters
• Compromise often possible

Demonstration Value: MEDIUM

• Shows individual/collective tension
• Relatable to many contexts
• Teaches accommodation principles

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Individual vs. Large Group (20+ people, organization, platform)

Characteristics:

• Significant power asymmetry
• Individual impact on many (e.g., whistleblower)
• Or organizational impact on individual (e.g., content moderation)

Examples:

• User appeals content removal decision (individual vs. platform)
• Whistleblower vs. corporation (disclosure vs. confidentiality)
• Citizen objects to municipal zoning decision

Deliberation Dynamics:

• Due process critical
• Transparency important (power imbalance)
• Precedent-setting potential high

Demonstration Value: HIGH

• Media appeal (underdog narrative)
• Governance relevance (platform/state power)
• Shows accountability mechanisms

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Small Group vs. Small Group

Characteristics:

• Peer relationship
• Resource competition
• Coordination challenges

Examples:

• Department A vs. Department B (budget allocation)
• Neighborhood block associations (parking dispute)
• Sports leagues vs. informal users (park booking priority)

Deliberation Dynamics:

• Neither has inherent authority
• Negotiation, not hierarchy
• Fairness procedures matter

Demonstration Value: VERY HIGH

• Demonstrates non-hierarchical deliberation perfectly
• Manageable complexity
• Clear stakeholder representation
• Relatable to many contexts (workplace, community, etc.)

Recommendation: Prioritize this scale for demonstrations

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Large Group vs. Large Group

Characteristics:

• Systemic conflict
• Institutional representation
• High stakes, broad impact

Examples:

• Labor union vs. employer association
• Environmental coalition vs. industry group
• Urban communities vs. rural communities (resource allocation)

Deliberation Dynamics:

• Requires formal representation
• Public interest high
• Outcomes affect many

Demonstration Value: MEDIUM-HIGH

• Relevant to governance
• But complex (many internal divisions within each group)
• May require extensive background

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Societal-Level (Cross-Cutting)

Characteristics:

• No clear "groups" (diffuse positions)
• Cultural/generational/regional divisions
• Abstract values tensions

Examples:

• Urban vs. rural values (zoning, agriculture policy)
• Generational conflicts (climate urgency, social programs)
• Regional differences (federal policy impacts)

Deliberation Dynamics:

• Difficult to identify "stakeholders" (everyone affected differently)
• Representation challenging
• Outcomes contested

Demonstration Value: LOW

• Too abstract for initial demonstration
• Stakeholders hard to identify
• Deliberation process unclear

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Global-Level

Characteristics:

• Nation-state actors
• Cultural blocs
• Extremely high complexity

Examples:

• Trade policy disputes (US-China, Global North-South)
• Climate negotiations (developed vs. developing nations)
• Digital governance (GDPR vs. Section 230)

Deliberation Dynamics:

• Diplomatic/treaty model
• Sovereignty concerns
• Power politics dominant

Demonstration Value: VERY LOW

• Far too complex for demonstration
• Audiences lack context
• Deliberation process not relatable

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Scale Selection Criteria

For Demonstration Scenarios:

Prioritize:

1. Small Group vs. Small Group (best demonstration value, non-hierarchical, manageable)
2. Individual vs. Large Group (media appeal, shows power/accountability)
3. Small Group vs. Large Group (if asymmetry manageable)

Avoid: 4. Societal-level (too abstract) 5. Global-level (too complex) 6. Individual vs. Individual (too small, low generalizability)

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3. Dimension 2: Conflict Type Taxonomy

Five Core Conflict Categories

Conflicts arise from different types of values tensions. Each category has distinct deliberation dynamics and demonstration characteristics.

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Category 1: RESOURCE ALLOCATION CONFLICTS

Definition: Disputes over distribution of finite resources (money, time, space, attention)

Characteristics:

• Zero-sum or trade-off structure (more for X = less for Y)
• Quantifiable stakes (often measurable)
• Efficiency vs. fairness tensions
• Utilitarian reasoning common

Subcategory 1.1: Monetary/Economic

Budget Distribution
├─ Which programs get funding? (education vs. infrastructure)
├─ Departmental allocation (R&D vs. marketing)
└─ Public goods provision (parks vs. police)

Wage Negotiation
├─ Labor vs. capital (profit-sharing)
├─ Pay equity (equal pay for equal work vs. market rates)
└─ Executive compensation (fairness vs. market competition)

Tax Policy
├─ Who pays? (progressive vs. flat tax)
├─ Who benefits? (redistribution vs. incentives)
└─ Intergenerational (debt burden on future generations)

Pricing Decisions
├─ Affordability vs. profitability (pharmaceutical pricing)
├─ Peak vs. off-peak (congestion pricing)
└─ Subsidized access (student discounts, sliding scale)

Moral Frameworks Often in Tension:

• Utilitarian: Maximize total welfare (efficiency)
• Egalitarian: Equal distribution (fairness)
• Libertarian: Market allocation (freedom)
• Needs-based: Distribute according to need (care ethics)

Demonstration Value: MEDIUM-HIGH

• Concrete, measurable
• Lower emotional charge than identity conflicts
• Shows value trade-offs clearly
• BUT: Can feel "solvable" via technocratic methods (may not showcase pluralism well)

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Subcategory 1.2: Physical Resources (Space, Natural Resources)

Land Use
├─ Park vs. housing development (recreation vs. shelter)
├─ Agricultural vs. residential (food vs. growth)
├─ Conservation vs. extraction (wilderness vs. resource use)
└─ Public vs. private (eminent domain)

Water Rights
├─ Agricultural vs. residential (farming vs. drinking)
├─ Urban vs. rural (city growth vs. local control)
├─ Environmental (ecosystem needs vs. human use)
└─ Interstate/international (upstream vs. downstream)

Energy Allocation
├─ Fossil fuels vs. renewables (reliability vs. sustainability)
├─ Grid priority (industrial vs. residential)
└─ Public vs. private (utility regulation)

Spectrum Allocation (Airwaves)
├─ Commercial vs. public broadcasting
├─ Emergency services vs. consumer (5G vs. public safety)
└─ National vs. global (satellite spectrum)

Moral Frameworks:

• Stewardship: Future generations' rights (environmental ethics)
• Property Rights: Owner autonomy (libertarian)
• Public Good: Common resources (communitarian)
• Needs-Based: Essential resources prioritized (care ethics)

Demonstration Value: MEDIUM

• Tangible, visualizable
• Local examples relatable (park usage)
• But can require technical knowledge (water rights complex)

Recommended Scenario: Public park usage priority (sports leagues vs. informal users)

• Small group vs. small group (ideal scale)
• Multiple allocation methods legitimate (lottery, first-come, pay, reservation)
• Low-stakes, high-demonstrability

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Subcategory 1.3: Time & Attention Resources

Curriculum Time (Educational)
├─ Which subjects taught? (STEM vs. humanities, vocational vs. academic)
├─ How much time per subject? (math hours vs. arts hours)
├─ Standardized test prep vs. enrichment
└─ Mandatory vs. elective (student choice vs. core requirements)

Meeting/Agenda Time (Organizational)
├─ Which topics discussed? (strategic vs. operational)
├─ Who gets speaking time? (seniority vs. equality)
└─ Urgent vs. important (crisis management vs. planning)

Media Coverage (Journalistic)
├─ Which stories run? (breaking news vs. investigative)
├─ Front page vs. buried (visibility, framing)
├─ Diverse perspectives vs. dominant narrative
└─ Sensational vs. important (clicks vs. public interest)

Platform Visibility (Algorithmic Prioritization)
├─ What content surfaces? (engagement vs. quality)
├─ Whose voices amplified? (popularity vs. marginalized perspectives)
├─ Advertising vs. organic (paid vs. earned reach)
└─ Local vs. global (geographic relevance)

Moral Frameworks:

• Meritocratic: Quality/importance determines allocation
• Egalitarian: Equal time/attention for all
• Democratic: Majority preference determines
• Epistemic: Expert judgment prioritizes (in education, journalism)

Demonstration Value: HIGH

• Affects everyone (everyone experiences time scarcity)
• Shows competing allocation principles clearly
• Curriculum time especially good (multiple legitimate frameworks)

Recommended Scenario: University lecture recording policy

• Accessibility (recordings help disabled students) vs.
• Pedagogy (attendance matters for learning) vs.
• IP (professor's content ownership) vs.
• Privacy (students visible in recordings)
• Multi-sided, shows cascading considerations

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Category 2: BELIEF SYSTEM / WORLDVIEW CONFLICTS

Definition: Disputes rooted in differing fundamental commitments (religious, ideological, epistemic)

Characteristics:

• High emotional investment (identity-constituting beliefs)
• Difficult to compromise (beliefs not easily split)
• Polarization risk (tribalism, in-group/out-group dynamics)
• Appeals to different authorities (scripture, science, tradition, reason)

WARNING: These conflicts require extreme care in demonstration - high risk of:

• Appearing to take sides
• Offending deeply-held beliefs
• Reinforcing polarization

Subcategory 2.1: Religious vs. Secular

Religious Accommodation (Institutional)
├─ Prayer space in workplace/school (accommodation vs. separation)
├─ Dietary requirements (halal/kosher in cafeterias, institutions)
├─ Dress codes (religious garb vs. uniform policies)
└─ Sabbath observance (work schedules, exam schedules)

Holiday Observance
├─ School calendar (which holidays recognized?)
├─ Public displays (Christmas tree, menorah, etc. on public property)
└─ Work time off (whose holidays prioritized?)

Moral Instruction (Educational)
├─ Sex education (abstinence vs. comprehensive, LGBTQ+ inclusion)
├─ Evolution vs. creation (science standards)
├─ Moral values (whose ethics taught?)
└─ Religious studies (academic vs. devotional)

Moral Frameworks:

• Theistic: God's commands ultimate authority
• Secular Humanism: Human flourishing, reason-based ethics
• Pluralist: Accommodate all (within limits)
• Separationist: Public sphere should be neutral (no religious influence)

Demonstration Value: LOW for MVP (too polarized, too risky)

• Recommendation: Avoid for initial demonstrations
• If attempted: Focus on accommodation cases (less polarized than prohibition cases)
• Example: Workplace prayer space (practical problem-solving frame) better than school curriculum battles

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Subcategory 2.2: Ideological (Political/Economic)

Economic Ideology
├─ Free market vs. regulation (libertarian vs. social democratic)
├─ Individual achievement vs. structural factors (meritocracy vs. systemic analysis)
├─ Growth vs. redistribution (expand pie vs. share pie)
└─ Private property vs. commons (ownership models)

Political Ideology
├─ Individual rights vs. collective welfare (liberty vs. solidarity)
├─ Tradition vs. progress (conservatism vs. progressivism)
├─ Nationalism vs. globalism (sovereignty vs. cosmopolitanism)
└─ Centralization vs. localism (federal vs. states' rights)

Demonstration Value: MEDIUM (depends on issue)

• Avoid: Partisan hot-button issues (abortion, gun control, immigration)
• Consider: Emerging issues before partisan alignment (e.g., remote work policies, AI regulation in early stages)
• Frame carefully: Present as value trade-offs, not tribal allegiances

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Subcategory 2.3: Epistemic (Knowledge/Truth)

Authority & Expertise
├─ Scientific consensus vs. alternative theories (climate, vaccines)
├─ Expert authority vs. lived experience (who knows best?)
├─ Credentials vs. experiential knowledge (PhD vs. practitioner)
└─ Peer review vs. public access (knowledge gatekeeping)

Evidence Standards
├─ What counts as proof? (empirical, anecdotal, intuitive)
├─ Burden of proof (precautionary vs. permissive)
├─ Replication crisis (what counts as verified?)
└─ Quantitative vs. qualitative (numbers vs. narratives)

Historical Interpretation
├─ Whose narrative? (victor's history vs. marginalized voices)
├─ Presentism vs. contextualism (judge past by present standards?)
├─ National myths vs. critical history (patriotism vs. honesty)
└─ Commemoration (monuments, renaming)

Moral Frameworks:

• Empiricism: Evidence-based, scientific method
• Lived Experience: Personal knowledge valid (feminist epistemology)
• Traditional: Inherited wisdom (Burkean conservatism)
• Critical: Question power structures in knowledge production

Demonstration Value: MEDIUM-HIGH for select cases

• Good: Methodological disputes in research (ethical, practical)
• Good: Evidence standards (how much proof required? precautionary principle)
• Avoid: Science denial cases (climate, vaccines - too polarized)

Recommended Scenario: Research ethics - preprint publication

• Speed (public access to findings quickly) vs.
• Quality control (peer review catches errors) vs.
• Equity (open access vs. paywalls)
• Academic community understands nuances, shows expert disagreement as legitimate

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Category 3: LEGAL / PROCEDURAL CONFLICTS

Definition: Disputes over rules, processes, jurisdiction, and interpretation

Characteristics:

• High demonstration value (shows deliberation in rule-making)
• Procedural fairness salient (how we decide matters)
• Rights vs. efficiency common tension
• Often shows legal formalism vs. equity tension

Subcategory 3.1: Rights-Based

Speech vs. Safety
├─ Hate speech policies (free expression vs. harm prevention)
├─ Misinformation (truth vs. censorship)
├─ Campus speakers (platforming vs. protest rights)
└─ Workplace speech (employee expression vs. employer reputation)

Privacy vs. Transparency
├─ FOIA requests (public records vs. privacy)
├─ Anonymity vs. accountability (online identities)
├─ Surveillance (security vs. privacy)
└─ Data collection (research vs. consent)

Due Process vs. Efficiency
├─ Expedited decisions (speed vs. fairness)
├─ Automated adjudication (scale vs. individual consideration)
├─ Appeals processes (finality vs. error correction)
└─ Burden of proof (who must prove what?)

Equality vs. Liberty
├─ Anti-discrimination laws (equal treatment vs. freedom of association)
├─ Affirmative action (equal opportunity vs. colorblindness)
├─ Accessibility mandates (inclusion vs. cost/autonomy)
└─ Compelled speech (non-discrimination vs. conscience)

Moral Frameworks:

• Rights-based (Deontological): Inviolable rights, side constraints
• Utilitarian: Outcomes matter most (rights as instruments)
• Communitarian: Rights balanced with community good
• Libertarian: Negative rights (non-interference) prioritized

Demonstration Value: VERY HIGH

• Shows non-hierarchical reasoning perfectly
• Rights conflicts are genuinely incommensurable (can't just "maximize rights")
• Procedural fairness widely valued (less partisan)
• Speech vs. safety particularly rich (multiple legitimate positions)

Recommended Scenario: Platform content moderation - algorithmic vs. human review

• Efficiency (AI scales) vs. Fairness (human judgment) vs. Consistency (algorithms) vs. Context (humans understand nuance)
• BUT: May be over-covered in media (less fresh)

Better Option: Algorithmic hiring transparency (detailed in Document 2)

• Efficiency vs. Fairness vs. Privacy vs. Accountability
• Timely, affects many, not yet polarized

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Subcategory 3.2: Jurisdictional / Procedural

Authority & Hierarchy
├─ Federal vs. state (who decides? marijuana laws, abortion)
├─ Organizational hierarchy (manager vs. employee, board vs. CEO)
├─ International vs. national (treaties vs. sovereignty)
└─ Expert vs. democratic (technocracy vs. popular will)

Precedent vs. Adaptation
├─ Strict interpretation vs. flexible (constitutional originalism vs. living document)
├─ Precedent binding vs. revisable (stare decisis vs. overturning)
├─ Rulebook literalism vs. spirit (following vs. creative interpretation)
└─ Zero tolerance vs. discretion (mandatory minimums vs. judicial discretion)

Contract Interpretation
├─ Letter vs. spirit (what was written vs. what was meant)
├─ Changed circumstances (force majeure, hardship)
├─ Good faith (implied duties vs. explicit only)
└─ Penalty vs. remedy (enforcement mechanisms)

Moral Frameworks:

• Formalism: Rules as written, predictability
• Equity: Fairness in specific case, context matters
• Democratic: Majoritarian (elected officials decide)
• Expertise: Technocratic (specialists decide)

Demonstration Value: MEDIUM-HIGH

• Good for showing practical wisdom (phronesis) vs. rule-following
• Relatable to anyone in organizations
• Can be dry if not grounded in concrete scenario

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Category 4: IDENTITY / RECOGNITION CONFLICTS

Definition: Disputes over group status, representation, historical redress, cultural recognition

Characteristics:

• HIGHEST emotional stakes (identity-constituting)
• HIGHEST pattern bias risk (can reinforce stereotypes, marginalize)
• Power dynamics central (historically marginalized groups)
• Zero-sum framing common (but false - recognition not scarce)

WARNING: These are the highest-risk scenarios for demonstrations. Require:

• Extensive cultural sensitivity review
• Stakeholder input from affected communities
• Careful framing to avoid tokenization
• Recommendation: AVOID for MVP, consider only after establishing credibility

Subcategory 4.1: Cultural Recognition

Language Policy
├─ Official languages (which recognized? bilingual requirements?)
├─ Minority language preservation (investment in dying languages)
├─ Education language (immersion vs. English-first)
└─ Government services (translation requirements)

Representation
├─ Who gets voice at table? (board diversity, advisory committees)
├─ Proportional vs. equal (population share vs. every group gets one seat)
├─ Descriptive vs. substantive (members of group vs. advancing group interests)
└─ Token vs. genuine (optics vs. power)

Naming & Symbols
├─ Monument controversy (remove, rename, contextualize?)
├─ Place names (Columbus, Washington - problematic figures)
├─ Team names/mascots (Indigenous peoples, appropriation)
└─ Holidays (Columbus Day vs. Indigenous Peoples' Day)

Cultural Appropriation
├─ Use of sacred symbols (headdresses, religious items)
├─ Art/fashion borrowing (line between appreciation and appropriation)
├─ Commodification (selling cultural practices)
└─ Gatekeeping vs. sharing (who decides what's acceptable?)

Demonstration Value: LOW for MVP (too risky, too fraught)

• These conflicts are real, important, and difficult
• But demonstrations risk:
  • Simplifying complex histories
  • Tokenizing affected groups
  • Causing vicarious harm
  • Appearing to "solve" what can't be solved in deliberation

Exception: Might work if:

• Stakeholders from affected groups co-design scenario
• Focus on procedural question (how to decide?) not substantive (what to decide?)
• Example: "How should a university decide about renaming a building?" (process) rather than "Should Columbus statue be removed?" (outcome)

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Subcategory 4.2: Group Status & Historical Redress

Historical Grievance & Reparations
├─ Slavery reparations (compensation for historical injustice)
├─ Indigenous land rights (treaties, sovereignty)
├─ Restitution (stolen art, property)
└─ Acknowledgment (formal apologies, truth commissions)

Protected Class Definitions
├─ Who qualifies for protection? (which groups recognized?)
├─ Immutable vs. voluntary (race vs. religion)
├─ Expanding categories (LGBTQ+, disability, neurodivergence)
└─ Intersectionality (overlapping identities, compounding disadvantage)

Minority Rights vs. Majority Rule
├─ Veto power (protect minorities from majority tyranny)
├─ Supermajority requirements (consensus vs. efficiency)
├─ Proportional representation (voting systems)
└─ Opt-out provisions (accommodation for minority practices)

Demonstration Value: VERY LOW (avoid for demonstration)

• These are the most difficult value conflicts
• Deep historical wounds, ongoing injustice
• Demonstrations risk:
  • Re-traumatization
  • False equivalence (treating oppression as "just another view")
  • Delegitimizing lived experience

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Category 5: SCIENTIFIC / TECHNICAL CONFLICTS

Definition: Disputes over technical standards, risk assessment, research ethics, methodological choices

Characteristics:

• Expertise matters (but expert disagreement possible)
• Precautionary principle often debated
• Quantifiable but uncertainty high
• Public values + technical judgment

Subcategory 5.1: Risk Assessment

Safety Standards
├─ How safe is safe enough? (acceptable risk levels)
├─ Cost-benefit analysis (value of life, QALY calculations)
├─ Precautionary vs. permissive (prove safe vs. prove harmful)
└─ Catastrophic vs. incremental (low-probability, high-impact events)

Environmental Impact
├─ Acceptable pollution levels (air quality, water quality)
├─ Habitat destruction vs. development (species protection)
├─ Climate policy (carbon budgets, adaptation vs. mitigation)
└─ Intergenerational (discount rates for future harm)

Health Policy
├─ Quarantine vs. freedom (pandemic response)
├─ Mandatory vaccination (herd immunity vs. bodily autonomy)
├─ Drug approval (efficacy standards, expedited review)
└─ Mental health holds (involuntary commitment criteria)

Technology Adoption
├─ Precautionary vs. permissive (prove safe vs. iterate quickly)
├─ Dual-use research (gain-of-function, AI capabilities)
├─ Genetic engineering (CRISPR, gene drives)
└─ Autonomous systems (self-driving cars, military drones)

Moral Frameworks:

• Precautionary: Prevent harm, err on side of caution
• Permissive: Innovation benefits, over-regulation stifles progress
• Risk-averse: Protect most vulnerable
• Utilitarian: Maximize expected value (cost-benefit)

Demonstration Value: MEDIUM

• Shows epistemic humility (uncertainty, expert disagreement)
• But can feel technocratic (less about values, more about facts)
• Avoid: Health crises (COVID, vaccines - too polarized, too traumatic)
• Consider: Technology governance (AI, biotech - emerging, not yet entrenched)

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Subcategory 5.2: Methodological & Research Ethics

Research Ethics
├─ Animal testing (scientific benefit vs. animal welfare)
├─ Human subjects (informed consent, vulnerable populations)
├─ Dual-use research (knowledge that could be weaponized)
└─ Data ownership (who owns research data? Indigenous knowledge?)

Data Collection & Privacy
├─ Surveillance for research (public health tracking, mobility data)
├─ Consent models (opt-in, opt-out, broad consent)
├─ Data sharing (open science vs. privacy)
└─ Commercial use (research data → products)

Publication Standards
├─ Preprints vs. peer review (speed vs. quality)
├─ Open access vs. paywalls (equity vs. sustainability)
├─ Replication requirements (when is a finding verified?)
└─ Registered reports (pre-commit to methods vs. flexibility)

Demonstration Value: HIGH for specialized audiences

• Academic/research community understands nuances
• Shows legitimate expert disagreement
• Demonstrates procedural deliberation (how should we decide standards?)
• Less media-salient but teaches principles well

Recommended Scenario: Preprint publication standards

• Speed (public access to COVID research) vs.
• Quality control (peer review prevents misinformation) vs.
• Equity (open access vs. journal paywalls)
• Shows epistemic humility, accommodation (tiered system emerged)

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Conflict Type Summary Table

Category	Demonstration Value	Safety Risk	Media Interest	Recommended for MVP?
1. Resource Allocation	Medium-High	Low	Medium	YES (park usage, curriculum time)
2. Belief System	Low-Medium	High	High	NO (too polarized)
3. Legal/Procedural	Very High	Low-Medium	Medium-High	YES (algorithmic hiring, lecture recording)
4. Identity/Recognition	Low	Very High	Very High	NO (too risky for MVP)
5. Scientific/Technical	Medium	Low-Medium	Medium	MAYBE (research ethics good, health policy avoid)

Key Insight: Legal/Procedural conflicts (Category 3) and Resource Allocation (Category 1) offer best demonstration opportunities for MVP:

• Lower emotional charge than belief/identity conflicts
• Demonstrate pluralistic reasoning clearly
• Relatable to broad audiences
• Lower pattern bias risk

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4. Dimension 3: Differentiating Attributes & Pattern Bias Risk

Pattern Bias Risk Framework

Definition: The risk that a demonstration scenario inadvertently reinforces harmful stereotypes, marginalizes vulnerable groups, or causes vicarious harm.

Why This Matters:

• Demonstrations have real-world impact (people see themselves in scenarios)
• Tractatus claims to prevent AI harms → must model ethical practices
• Pattern bias is insidious → requires proactive analysis, not just "good intentions"

Demographic Dimensions Analysis

Age

Examples:

• Youth vs. elders (climate urgency, social security policy)
• Working age vs. retired (healthcare priorities)
• Digital natives vs. immigrants (technology policy)
• Generational wealth (housing affordability)

Pattern Bias Risks:

• Ageism: "Boomers are selfish," "Millennials are entitled"
• Stereotype reinforcement: Old = technophobic, young = irresponsible
• Generational warfare: Presenting as zero-sum (misleading)

Risk Level: MEDIUM

• Age-based scenarios can work if carefully framed
• Focus on structural issues (retirement system design) not character ("old people hoard wealth")
• Avoid pitting generations against each other unnecessarily

Mitigation:

• Show intra-generational diversity (not all boomers same)
• Frame as policy design question, not moral failing
• Include voices from multiple age cohorts as stakeholders

---

Education Level

Examples:

• Credentialed vs. experiential knowledge (who's the expert?)
• Academic vs. vocational (curriculum priorities, funding)
• Literate vs. oral traditions (documentation requirements)
• Expert vs. lay (who gets to decide?)

Pattern Bias Risks:

• Elitism: Privileging academic knowledge over practical
• Anti-intellectualism: Dismissing expertise as "out of touch"
• Credentialism: Ignoring lived experience, informal knowledge

Risk Level: MEDIUM

• Education conflicts can showcase epistemic pluralism (multiple valid ways of knowing)
• But risk reinforcing hierarchies (PhD vs. high school diploma)

Mitigation:

• Frame as complementary knowledge types, not hierarchical
• Show cases where formal credentials insufficient (local knowledge matters)
• Avoid scenarios where education level correlates with "rightness"

Good Example: Research ethics - patient/community input

• Medical researchers (credentialed) + patients (experiential) both essential
• Neither can be substituted for the other
• Shows epistemic humility

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Socioeconomic Status (Class)

Examples:

• Wealth disparity (progressive taxation, wealth tax)
• Precariat vs. secure employment (gig economy classification)
• Urban vs. rural (infrastructure investment)
• Property owners vs. renters (development policy, zoning)

Pattern Bias Risks:

• Class resentment: "Rich people are greedy," "Poor people are lazy"
• Just-world fallacy: Wealth = merit, poverty = failure
• Rural/urban divide: Coastal elites vs. heartland (geographic class)

Risk Level: MEDIUM

• Economic conflicts are less personal than identity (situational, not intrinsic)
• But class is deeply stratified → power dynamics matter
• Can trigger resentment (especially in polarized contexts)

Mitigation:

• Focus on structural issues (housing policy, labor law) not individuals
• Avoid "deserving vs. undeserving" framing
• Show cross-class coalitions (not always rich vs. poor)

Good Example: Remote work location pay

• Urban workers (high cost of living) vs. rural workers (lower costs)
• Employers (market efficiency) vs. workers (fairness)
• Not: Rich vs. poor (economic, but not class warfare)

---

Geographic

Examples:

• National origin (immigration policy, citizenship)
• Regional (coastal vs. inland, metro vs. rural)
• Linguistic (anglophone vs. non-anglophone)
• Climate zone (adaptation priorities, resource allocation)

Pattern Bias Risks:

• Xenophobia: "Foreigners" as threat
• Urban bias: Rural as backward, urban as elitist
• Regionalism: North vs. South, coast vs. heartland stereotypes

Risk Level: LOW-MEDIUM

• Geographic differences less personal than race, gender, religion
• But can proxy for other dimensions (urban/rural → class, culture)
• Immigration = high risk (entangled with race, nationalism)

Mitigation:

• Focus on geographic policy questions (infrastructure, services) not character
• Avoid "urban good, rural bad" or vice versa framings
• Show diversity within regions (rural is not monolithic)

Good Example: Municipal services allocation

• Downtown vs. neighborhoods (transit, parks)
• Geographic but not identity-based
• Shows resource allocation clearly

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Race & Ethnicity

Examples:

• Majority vs. minority (representation, power)
• Settler vs. Indigenous (land rights, sovereignty)
• Diaspora vs. homeland (political allegiance, identity)
• Multiracial (identity category boundaries, census)

Pattern Bias Risks:

• Racism: Reinforcing stereotypes, marginalizing voices
• Colorblindness: Ignoring historical and structural racism
• Tokenization: Treating diverse groups as monolithic
• Appropriation: Using cultural conflict for demonstration without accountability

Risk Level: VERY HIGH

Recommendation for MVP: AVOID

• Race is foundational to US (and many other) societies → conflicts are real and urgent
• But demonstrations risk:
  • Oversimplifying centuries of oppression
  • False equivalence (treating racism as "just a view")
  • Re-traumatization for people of color
  • Exploitation (using pain for demonstration purposes)

When to Attempt (if ever):

• Only with co-design by racial justice organizations
• Focus on procedural questions (how to make decisions about equity policies?) not substantive
• After establishing credibility with lower-risk scenarios
• With extensive cultural sensitivity review

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Gender & Sexuality

Examples:

• Cisgender vs. transgender (bathroom policy, sports inclusion)
• Heteronormative vs. LGBTQ+ (marriage recognition, adoption)
• Binary vs. non-binary (form design, pronouns)
• Reproductive autonomy (abortion access, surrogacy)

Pattern Bias Risks:

• Transphobia: Denying trans identities, "debate my existence"
• Heteronormativity: Assuming straight/cis as default, "normal"
• Misogyny: Gender bias in framing (women as emotional, men as rational)
• Erasure: Ignoring non-binary, intersex people

Risk Level: VERY HIGH

Recommendation for MVP: AVOID

• Gender/sexuality conflicts are highly polarized (culture war flashpoint)
• Trans rights especially fraught → "both sides" framing delegitimizes trans existence
• Demonstrations risk causing harm to already-marginalized groups

Exception:

• Procedural questions in specific contexts might work
• Example: "How should an organization design a form to be inclusive of gender diversity?" (practical, design-focused)
• Still risky → only attempt with LGBTQ+ stakeholder input

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Ability (Disability, Neurodivergence)

Examples:

• Neurotypical vs. neurodivergent (workplace norms, accommodation)
• Able-bodied vs. disabled (accessibility standards, universal design)
• Sensory (visual, auditory accommodations)
• Cognitive (information design, simplification)

Pattern Bias Risks:

• Ableism: Treating disability as deficit, not difference
• Burden framing: Accessibility as "cost" not "right"
• Medical model: Disability as individual problem, not social construct
• Inspiration porn: Exploiting disabled people's stories

Risk Level: MEDIUM-HIGH

• Disability conflicts are important (accessibility, inclusion)
• But require disability justice framing (social model, not medical model)
• Risk of framing accessibility as "nice to have" not "civil right"

Mitigation:

• Use social model (barriers are in environment, not person)
• Frame as universal design (benefits everyone, not just "special accommodation")
• Include disabled stakeholders in scenario design (nothing about us without us)

Good Example: University lecture recording

• Accessibility (recordings help disabled students) is ONE consideration among many
• Not framed as "accommodate disabled vs. professor rights"
• Shows accessibility as part of pedagogical design, not burden

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Pattern Bias Risk Assessment Matrix

Dimension	Risk Level	Safe for MVP?	Notes
Age	Medium	⚠️ With care	Avoid generational warfare framing
Education	Medium	⚠️ With care	Show epistemic pluralism, not hierarchy
Socioeconomic	Medium	✅ YES	Economic, not identity; avoid class resentment
Geographic	Low-Medium	✅ YES	Especially local (urban vs. rural okay if balanced)
Race/Ethnicity	Very High	❌ NO	Avoid for MVP; only with co-design later
Gender/Sexuality	Very High	❌ NO	Highly polarized; risk delegitimizing identities
Ability	Medium-High	⚠️ Maybe	Requires disability justice framing; include disabled stakeholders

General Mitigation Principles

1. Avoid Centering Vulnerable Groups

• Don't make demonstrations "about" marginalized identities
• If a dimension is present, it should be incidental not central

2. Structural vs. Individual Framing

• Focus on systems, policies, structures
• Avoid framing as individual moral failings

3. Intra-Group Diversity

• Show diversity within groups (not all X are the same)
• Avoid monolithic portrayals

4. Power-Aware

• Acknowledge historical and structural power imbalances
• Don't "both sides" oppression

5. Stakeholder Input

• If a marginalized group is affected, include their input in design
• Co-design, not extraction

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5. Dimension 4: Media Interest Patterns

Why Media Patterns Matter

Strategic Demonstration Considerations:

1. Public Salience

• If audiences don't care about the issue, demonstration won't resonate
• But: We can't just follow hype (avoid clickbait)

2. Polarization Level

• High salience + low polarization = IDEAL (everyone cares, not entrenched)
• High salience + high polarization = RISKY (hard to appear neutral)
• Low salience + low polarization = BORING (no one cares)

3. Emerging vs. Entrenched

• Emerging issues = opportunity (shape conversation before battle lines harden)
• Entrenched issues = difficult (positions already hardened, tribalism high)

4. Demonstration Timing

• Some issues "hot" now but will cool (COVID restrictions)
• Some perennial (privacy vs. security)
• Target: Rising interest, not yet peaked

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Media Pattern Analysis Methods

(See Document 4: Media Pattern Research Guide for full methodology)

Quick Assessment Tools:

1. Google Trends (5-year lookback)

• Search volume over time (rising, stable, declining?)
• Geographic distribution (global, regional, local?)
• Related queries (what context?)

2. News Coverage Scan

• Major outlets (NYT, Guardian, BBC, etc.)
• Article count trends (increasing attention?)
• Partisan divide (liberal vs. conservative framing different?)

3. Academic Literature

• Google Scholar search volume
• Recent publications in top journals
• Interdisciplinary interest (ethics, law, CS, etc.)

4. Regulatory Activity

• Legislation introduced (bills, hearings)
• Agency actions (rulemaking, guidance)
• International (EU AI Act, GDPR, etc.)

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Current Media Landscape (2024-2025)

High-Salience Topics:

AI Ethics & Governance

• Algorithmic bias (hiring, lending, criminal justice)
• Content moderation (free speech, misinformation)
• Deepfakes & synthetic media
• Autonomous systems (self-driving cars, drones)
• Status: High interest, LOW-MEDIUM polarization (still debating frameworks)
• Recommendation: EXCELLENT for demonstrations

Labor & Work

• Remote work (return to office, geographic pay, flexibility)
• Gig economy (worker classification, benefits)
• AI automation (job displacement, retraining)
• Unionization (Starbucks, Amazon)
• Status: High interest, MEDIUM polarization (partisan but not tribal)
• Recommendation: GOOD for demonstrations (especially remote work, less partisan than unions)

Climate & Environment

• Carbon pricing (cap-and-trade, carbon tax)
• Adaptation vs. mitigation (spend on prevention or resilience?)
• Climate migration (borders, refugees)
• Geoengineering (technological solutions vs. risk)
• Status: High interest, HIGH polarization (climate denial vs. activism)
• Recommendation: AVOID for MVP (too polarized, though important)

Platform Governance

• Social media content moderation (hate speech, misinformation)
• Algorithmic amplification (engagement vs. quality)
• Privacy (data collection, tracking)
• Child safety (age verification, parental consent)
• Status: High interest, HIGH polarization (Section 230, "censorship" debates)
• Recommendation: RISKY (very polarized, tribalized)

Education & Curriculum

• School curriculum (CRT, LGBTQ+ content, book bans)
• Higher ed (student debt, free college, affirmative action)
• Online learning (access, quality)
• AI in education (ChatGPT, essay detectors)
• Status: High interest, VERY HIGH polarization (culture war flashpoint)
• Recommendation: AVOID (except technical questions like AI detectors, recording policies)

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Emerging Issues (Opportunity Window)

These issues have RISING interest but NOT YET polarized into tribal camps:

1. Algorithmic Hiring & Workplace AI ⭐⭐⭐⭐⭐

• Google Trends: Rising 2020-2024
• News: Bias cases (Amazon, Facebook) drove coverage
• Regulatory: NYC Local Law 144 (2023), EU AI Act
• Academic: Growing fairness, explainability literature
• Polarization: LOW (not yet partisan)
• Assessment: IDEAL for demonstration

2. Remote Work Policies ⭐⭐⭐⭐

• Google Trends: Spiked 2020 (COVID), still elevated
• News: Return-to-office battles, geographic pay debates
• Regulatory: Minimal (mostly private sector)
• Polarization: LOW-MEDIUM (some partisan framing, but mostly practical)
• Assessment: EXCELLENT for demonstration

3. Digital Inheritance & Data After Death ⭐⭐⭐

• Google Trends: Slowly rising
• News: Occasional human-interest stories
• Regulatory: Patchwork (RUFADAA in some US states)
• Polarization: VERY LOW (not partisan)
• Assessment: GOOD but low salience (audiences may not care yet)

4. Gig Worker Classification ⭐⭐⭐⭐

• Google Trends: Stable, moderate interest
• News: Prop 22 (California), Uber/Lyft battles
• Regulatory: Active (state-by-state, Europe)
• Polarization: MEDIUM (labor vs. capital, but not tribal)
• Assessment: GOOD (some partisan overtones, but deliberation possible)

5. Synthetic Biology & CRISPR Ethics ⭐⭐⭐

• Google Trends: Moderate, stable
• News: Periodic (gene drive, CRISPR babies scandal)
• Regulatory: Developing (FDA, international)
• Polarization: LOW (expert debate, public not engaged)
• Assessment: GOOD for specialized audiences (less mass appeal)

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Entrenched Issues (Avoid for MVP)

These issues are HIGHLY POLARIZED, tribal:

❌ Abortion (culture war, identity-level) ❌ Gun control (Second Amendment, partisan identity) ❌ Immigration (borders, nationalism, race) ❌ Climate change (denial vs. activism, partisan) ❌ Vaccine mandates (bodily autonomy vs. public health, post-COVID trauma) ❌ Critical Race Theory (education culture war) ❌ Trans rights (gender identity, bathroom/sports battles)

Why avoid:

• Positions hardened into tribal identities
• "Both sides" framing delegitimizes one side (often oppressed group)
• Audiences bring heavy baggage
• Deliberation unlikely to be seen as legitimate

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Media Pattern Summary for Top Scenarios

Scenario	Google Trends	News Coverage	Regulatory	Polarization	Recommendation
Algorithmic Hiring	Rising	High (bias cases)	Emerging (NYC, EU)	LOW	⭐⭐⭐⭐⭐ IDEAL
Remote Work Pay	Elevated	High (RTO battles)	Minimal	LOW-MED	⭐⭐⭐⭐ EXCELLENT
Park Usage Priority	Low	Local only	Local	VERY LOW	⭐⭐⭐ GOOD (low salience)
Open-Source Licensing	Stable	Tech community	Minimal	LOW	⭐⭐⭐ GOOD (niche)
Lecture Recording	Low	Higher ed only	FERPA, ADA	LOW	⭐⭐⭐⭐ GOOD (education context)

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6. Scenario Taxonomy

Based on the four-dimensional analysis above, scenarios are classified into three tiers:

Tier 1: Strong Candidates for MVP (Score 20+/25)

These scenarios excel across multiple dimensions: demonstrable, safe, generalizable, media-relevant, and showcase pluralistic reasoning.

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Scenario 1.1: Algorithmic Hiring Transparency ⭐⭐⭐⭐⭐

Conflict: Should companies be required to disclose how AI algorithms screen job applicants?

Scale: Individual (job seeker) vs. Large Group (employers, platform companies)

Conflict Type: Legal/Procedural (rights-based)

• Efficiency (AI screening saves time/money) vs.
• Fairness (candidates deserve to know selection criteria) vs.
• Privacy (what data is collected/used?) vs.
• Accountability (can bias be audited?) vs.
• Innovation (transparency requirements may stifle progress)

Moral Frameworks in Tension:

• Consequentialist (Utilitarian): Maximize hiring quality, minimize cost → proprietary algorithms optimal
• Rights-based (Deontological): Candidates have right to know basis of decisions affecting them
• Care Ethics: Dignity in job search, trust between employer-candidate
• Libertarian: Property rights in algorithms, freedom to use proprietary systems
• Communitarian: Labor market fairness affects social mobility, collective good

Stakeholders:

• Job seekers (especially historically disadvantaged groups)
• Employers (HR departments, hiring managers)
• AI/ATS vendors (technology companies)
• Civil rights organizations
• Labor advocates
• Regulators (EEOC, FTC, EU)

Demonstration Value:

• Demonstrability: 4/5 (requires brief AI screening explanation, then clear)
• Safety: 5/5 (economic, not identity-based; job seeking relatable, not traumatic)
• Generalizability: 5/5 (applicable to all algorithmic decision contexts)
• Media Appeal: 5/5 (timely, high interest, bias cases drove coverage)
• Pluralism Showcase: 5/5 (multiple frameworks, genuine incommensurability)

Total Score: 24/25

Why This is Ideal:

• Timely: NYC Local Law 144 (2023), EU AI Act, rising interest
• Not yet polarized: Before partisan battle lines harden
• Multiple legitimate frameworks: Not obvious which should "win"
• Affects many people: Millions experience AI hiring screening
• Demonstrates accommodation: Tiered transparency model emerges naturally
• Generalizable: Teaches algorithmic accountability broadly

Recommended as PRIMARY demonstration scenario (see Document 2 for deep-dive)

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Scenario 1.2: Remote Work Location-Based Pay ⭐⭐⭐⭐

Conflict: Should companies pay employees differently based on where they live when working remotely?

Scale: Individual (workers) vs. Large Group (employers) & Small Group vs. Small Group (urban workers vs. rural workers)

Conflict Type: Resource Allocation (economic)

• Market efficiency (pay reflects local cost of living) vs.
• Equal pay for equal work (same job, same pay) vs.
• Geographic equity (rural economic development) vs.
• Recruiting/retention (talent competition)

Moral Frameworks in Tension:

• Free Market (Libertarian): Wages determined by supply/demand, local markets differ
• Egalitarian: Equal pay for equal work, location irrelevant
• Utilitarian: Maximize total welfare (company competitiveness + worker welfare)
• Communitarian: Regional development, strengthen rural economies

Stakeholders:

• Remote workers in high-cost areas (San Francisco, New York)
• Remote workers in low-cost areas (rural, small cities)
• Employers (multinational corporations, startups)
• Geographic communities (urban, rural economic development groups)
• Compensation consultants

Demonstration Value:

• Demonstrability: 5/5 (immediately relatable, concrete)
• Safety: 5/5 (economic, geographic dimension less personal than race/gender)
• Generalizability: 4/5 (teaches economic fairness principles)
• Media Appeal: 4/5 (post-pandemic relevance, RTO battles)
• Pluralism Showcase: 4/5 (multiple frameworks, though utilitarian reasoning strong)

Total Score: 22/25

Why This is Strong:

• Timely: Post-pandemic remote work debates ongoing
• Relatable: Many people experienced remote work, understand trade-offs
• Economic not identity: Less emotional charge than identity conflicts
• Shows accommodation: Hybrid models (base pay + COLA) emerging
• Not tribal: Not yet aligned with partisan identities

Recommended as SECONDARY demonstration scenario

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Scenario 1.3: Public Park Usage Priority ⭐⭐⭐⭐

Conflict: How should public parks allocate space between organized sports leagues and informal users (pickup games, picnics, walking)?

Scale: Small Group vs. Small Group (ideal for non-hierarchical demonstration)

Conflict Type: Resource Allocation (physical space)

• Efficiency (organized leagues maximize field use) vs.
• Equal access (first-come-first-served fairness) vs.
• Community benefit (youth sports vs. family time) vs.
• Conservation (minimize field wear)

Moral Frameworks in Tension:

• Utilitarian: Most users served (organized leagues pack more people per hour)
• Egalitarian: Equal access for all (no prioritization)
• Communitarian: Youth development (invest in children's sports)
• Libertarian: Pay for reservations (market allocation)
• Virtue Ethics: Civic space for spontaneous community gathering

Stakeholders:

• Youth sports leagues (soccer, baseball, etc.)
• Adult sports leagues
• Families (picnics, informal play)
• Informal users (frisbee, walking, etc.)
• Environmental advocates (field conservation)
• Municipal parks department

Demonstration Value:

• Demonstrability: 5/5 (can draw diagram, immediately visual)
• Safety: 5/5 (infrastructure, no vulnerable groups)
• Generalizability: 4/5 (resource allocation principles)
• Media Appeal: 2/5 (local interest only, low national salience)
• Pluralism Showcase: 5/5 (multiple allocation methods legitimate)

Total Score: 21/25

Why This is Strong:

• Perfect scale: Small group vs. small group (non-hierarchical)
• Concrete: Everyone understands park usage
• Low-stakes: No one's life depends on this (safe for demonstration)
• Multiple solutions: Lottery, reservation, pay, first-come, hybrid
• Shows negotiation: Natural accommodation (time-sharing, designated zones)

Limitation:

• Low media salience (local issue)
• But: Excellent teaching tool for deliberation principles

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Scenario 1.4: University Lecture Recording Policy ⭐⭐⭐⭐

Conflict: Should universities require professors to record lectures and make them available to students?

Scale: Individual (professors) vs. Large Group (students, administration)

Conflict Type: Legal/Procedural + Resource Allocation (time/attention)

• Accessibility (recordings help disabled students) vs.
• Pedagogy (attendance matters for learning) vs.
• Intellectual property (professor's content ownership) vs.
• Privacy (students visible in recordings) vs.
• Workload (additional labor for recording/editing)

Moral Frameworks in Tension:

• Rights-based: Disability accommodation (ADA compliance)
• Epistemic (Pedagogical): Teaching effectiveness (in-person learning superior?)
• Property Rights: Faculty own their lectures (IP)
• Utilitarian: Maximize learning outcomes
• Care Ethics: Student welfare (flexibility, support)

Stakeholders:

• Students with disabilities
• General student body
• Faculty (professors, instructors)
• University administration
• IT departments (recording infrastructure)
• Legal (FERPA, ADA compliance)

Demonstration Value:

• Demonstrability: 4/5 (requires brief higher ed context, then clear)
• Safety: 5/5 (no vulnerable groups centered, academic context)
• Generalizability: 4/5 (workplace recording, remote work parallels)
• Media Appeal: 3/5 (higher ed community interest, less broad)
• Pluralism Showcase: 5/5 (multiple frameworks, cascading considerations)

Total Score: 21/25

Why This is Strong:

• Multi-sided: Not binary (record vs. don't), many considerations
• Shows complexity: Accessibility is important BUT not the only value
• Accommodation: Hybrid solutions (record some lectures, not all; opt-in; etc.)
• Educational context: Teaches deliberation in institutional setting

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Scenario 1.5: Open-Source Software Licensing Disputes ⭐⭐⭐

Conflict: Which open-source license should a project adopt? Permissive (BSD, MIT) vs. Copyleft (GPL)?

Scale: Small Group (project maintainers) vs. Large Group (user community, corporate adopters)

Conflict Type: Legal/Procedural + Ideological

• Freedom (permissive licenses maximize user freedom) vs.
• Reciprocity (copyleft ensures sharing back) vs.
• Commercial sustainability (dual licensing enables revenue) vs.
• Community building (which license attracts contributors?)

Moral Frameworks in Tension:

• Libertarian: Maximum freedom (permissive licenses)
• Reciprocity (Communitarian): Share-alike (copyleft)
• Consequentialist: Which license produces best outcomes? (most adoption? most contribution?)
• Deontological: Moral duty to share knowledge (copyleft) or respect autonomy (permissive)?

Stakeholders:

• Individual developers
• Corporate users (large tech companies)
• Open-source advocates (FSF, OSI)
• End users (benefit from open-source software)

Demonstration Value:

• Demonstrability: 3/5 (requires technical knowledge of licensing)
• Safety: 5/5 (no vulnerable groups, ideological but not partisan)
• Generalizability: 4/5 (intellectual property, commons governance)
• Media Appeal: 2/5 (tech community interest, niche to general public)
• Pluralism Showcase: 5/5 (genuinely incommensurable values)

Total Score: 19/25

Why This is Good:

• Ideological but not partisan: Not aligned with political tribes
• Technical community: Understands nuances, appreciates deliberation
• Legitimate disagreement: No "right answer," both positions defensible
• Generalizable: IP, commons, knowledge-sharing broadly

Limitation:

• Niche audience (tech community)
• Requires background knowledge

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Tier 2: Worth Considering (Score 15-19/25)

These scenarios have strengths but also limitations (lower media interest, more specialized, or moderate complexity).

Scenario 2.1: Community Garden Plot Allocation (Score: 18/25)

Conflict: How to allocate limited community garden plots among applicants?

Allocation Methods:

• Lottery (fairness, random)
• First-come-first-served (reward initiative)
• Need-based (prioritize low-income, no yard access)
• Seniority (reward long-term community members)
• Contribution-based (prioritize active volunteers)

Why Consider:

• Multiple legitimate allocation methods
• Small-scale, concrete
• Shows procedural deliberation

Limitations:

• Low media salience (very local)
• May feel trivial to some audiences

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Scenario 2.2: Neighborhood Traffic Calming (Score: 17/25)

Conflict: Should a neighborhood install speed bumps?

Considerations:

• Safety (slow traffic, protect children/pedestrians) vs.
• Emergency access (ambulances, fire trucks slowed) vs.
• Property values (noise from speed bumps, desirability) vs.
• Driving convenience (residents commute time increased)

Why Consider:

• Local governance example
• Multiple stakeholders with legitimate concerns
• Shows trade-off analysis

Limitations:

• Low media interest
• Outcome may seem "solvable" by expert traffic engineers (less pluralism)

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Scenario 2.3: Workplace Scent-Free Policy (Score: 16/25)

Conflict: Should a workplace implement a scent-free policy?

Considerations:

• Health (chemical sensitivity, allergies) vs.
• Personal expression (perfume, cologne, grooming products) vs.
• Cultural practices (some cultures use incense, oils) vs.
• Enforcement challenges (how to monitor, what counts?)

Why Consider:

• Shows accommodation complexity
• Health + culture + expression dimensions
• Relatable to anyone in workplace

Limitations:

• Small-scale (workplace only)
• May feel like "etiquette" not governance

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Tier 3: Avoid for MVP (Pattern Bias Risk or Too Polarized)

These scenarios are important but too risky for initial demonstrations due to:

• Vulnerable populations centered
• High polarization (tribal, partisan)
• Vicarious harm potential
• Complexity requiring extensive background

Scenarios to Avoid:

❌ Mental Health Crisis Intervention (privacy vs. safety)

• Reason: Vicarious harm risk, re-traumatization potential, vulnerable population

❌ School Curriculum Content (CRT, sex ed, evolution)

• Reason: Culture war flashpoint, highly polarized, children involved

❌ Immigration Policy (borders, citizenship, enforcement)

• Reason: Entangled with race, nationalism, highly partisan

❌ Abortion Access (rights, autonomy, religious beliefs)

• Reason: Tribal identity, moral absolutism common, polarized

❌ Affirmative Action (race-conscious admissions/hiring)

• Reason: Race-based, legally contentious, polarized

❌ Gender Transition (Youth) (medical, parental rights, identity)

• Reason: Vulnerable population (trans youth), highly polarized, "debate my existence"

❌ Climate Policy (carbon pricing, green energy mandates)

• Reason: Highly polarized (climate denial vs. activism), partisan

❌ Vaccine Mandates (public health vs. bodily autonomy)

• Reason: Post-COVID trauma, highly polarized, health anxiety

❌ Police Funding / Defund Police (safety, justice, race)

• Reason: Entangled with race, policing trauma, polarized

❌ Gun Control (Second Amendment, restrictions)

• Reason: Tribal identity, partisan, entrenched

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7. Strategic Selection Criteria

Decision Matrix for Scenario Selection

When choosing demonstration scenarios, apply these criteria systematically:

1. Safety First (30% weight)

• No vulnerable populations centered
• No vicarious harm risk
• No re-traumatization potential
• Pattern bias mitigated

2. Demonstrability (20% weight)

• Explainable in <5 minutes
• Concrete, visualizable
• Stakeholders clearly identifiable
• Outcomes understandable

3. Pluralism Showcase (25% weight)

• Multiple legitimate moral frameworks
• Genuine incommensurability (not just preferences)
• Non-hierarchical resolution possible
• Moral remainder documentable

4. Media Relevance (15% weight)

• Timely, current interest
• Not yet polarized into tribal camps
• Emerging issues preferred
• Audience salience

5. Generalizability (10% weight)

• Teaches broader principles
• Applicable to other contexts
• Shows deliberation process transferability

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Application of Criteria to Top Scenarios

Scenario	Safety (30%)	Demonstrability (20%)	Pluralism (25%)	Media (15%)	Generalizability (10%)	Total (100%)
Algorithmic Hiring	30/30	16/20	25/25	15/15	10/10	96/100 ⭐⭐⭐⭐⭐
Remote Work Pay	30/30	20/20	20/25	12/15	8/10	90/100 ⭐⭐⭐⭐
Park Usage	30/30	20/20	25/25	3/15	8/10	86/100 ⭐⭐⭐⭐
Lecture Recording	30/30	16/20	25/25	6/15	8/10	85/100 ⭐⭐⭐⭐
Open-Source Licensing	30/30	12/20	25/25	3/15	8/10	78/100 ⭐⭐⭐

Interpretation:

• Algorithmic Hiring scores highest (96/100) → Primary demonstration scenario
• Remote Work Pay strong second (90/100) → Secondary scenario
• Park Usage excellent for teaching (86/100) but low media salience
• Lecture Recording shows complexity well (85/100), niche audience
• Open-Source Licensing good for tech community (78/100), requires background

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8. Recommendations

For MVP Implementation

Primary Scenario: Algorithmic Hiring Transparency

• Demonstrates all key principles of PluralisticDeliberationOrchestrator
• Timely, media-relevant, not yet polarized
• Multiple moral frameworks clearly in tension
• Safe (economic, not identity-based)
• Generalizable to all algorithmic decision contexts

Secondary Scenario: Remote Work Location-Based Pay

• Complements primary (both AI/tech-adjacent, both economic)
• Shows geographic dimension (not just algorithmic)
• Relatable post-pandemic context
• Safe, not identity-based

Teaching Scenario (Optional): Public Park Usage Priority

• Excellent for demonstrating deliberation process
• Perfect scale (small group vs. small group)
• Low-stakes, accessible
• Use for workshops, training

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For Future Expansion

After establishing credibility with MVP:

Phase 2 (6-12 months):

• Lecture Recording Policy (educational context, procedural complexity)
• Research Ethics (preprint publication, open access)
• Traffic Calming (local governance, community engagement)

Phase 3 (12-24 months, with co-design):

• Workplace Accommodation (disability justice framing, stakeholder input)
• Cultural Recognition (procedural focus: how to decide about renaming?)
• Only if extensive cultural sensitivity review + co-design

Never Attempt (Too Risky):

• Mental health crises (vicarious harm)
• Abortion, immigration, gun control (tribal, polarized)
• Trans rights, race-based policies (vulnerable groups, polarized)

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9. Next Steps

Immediate (Complete This Session)

1. ✅ Document strategic framework (this document)
2. Create deep-dive analysis of Algorithmic Hiring (Document 2)
3. Develop evaluation rubric (Document 3)
4. Document media research methods (Document 4)
5. Plan refinement roadmap (Document 5)
6. Session handoff (Document 6)

Short-Term (Next 2-4 Weeks)

1. Stakeholder Mapping Deep-Dive

   • For algorithmic hiring + remote work pay
   • Identify real organizations who could represent perspectives
   • Research actual positions taken on similar issues

2. Rubric Calibration

   • Have 3-5 people independently score same 10 scenarios
   • Measure inter-rater reliability
   • Refine criteria based on disagreements

3. Media Research Execution

   • Complete full media pattern analysis for top 2 scenarios
   • Google Trends, news coverage, academic literature, regulatory activity
   • Document findings

Medium-Term (1-3 Months)

1. Pilot Testing

   • Interview 10-15 people from target audience
   • Present scenarios, gather feedback
   • Measure: Which immediately understood? Which confused?

2. Cultural Sensitivity Review

   • Convene diverse focus group (age, geography, culture)
   • Review scenarios for blind spots, offensive framings
   • Revise based on feedback

3. Expert Consultation

   • Political philosophers (pluralism framework sound?)
   • Ethicists (moral framework representations accurate?)
   • Deliberative democracy practitioners (process realistic?)

Long-Term (3-6 Months)

1. Scenario Database

   • Expand to 15-20 scored scenarios
   • Build searchable database with metadata
   • Tag by: conflict type, scale, moral frameworks, media patterns

2. Scenario Generator (Stretch Goal)

   • Input: conflict type, stakeholder structure, values in tension
   • Output: Suggested scenario outlines
   • Use for rapid prototyping of new demonstrations

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Conclusion

This strategic framework provides a rigorous, theory-grounded approach to selecting demonstration scenarios for the PluralisticDeliberationOrchestrator.

Key Insights:

1. Conflict type matters more than scale - Legal/procedural and resource allocation conflicts are safer and more demonstrable than identity/recognition conflicts

2. Emerging issues offer best opportunities - Before polarization hardens into tribal identities

3. Safety first - No vulnerable populations centered, minimize vicarious harm

4. Small group vs. small group is ideal scale - Demonstrates non-hierarchical deliberation perfectly

5. Algorithmic hiring is the standout scenario - Timely, safe, generalizable, demonstrates pluralism clearly

This framework ensures:

• Ethical demonstration practices (pattern bias risk assessment)
• Strategic media relevance (emerging issues, timely)
• Theoretical rigor (foundational pluralism, genuine incommensurability)
• Practical usability (clear evaluation criteria, systematic scoring)

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References

1. Tractatus Framework - Pluralistic Values Deliberation Plan v2 (2025)
2. Isaiah Berlin - "Two Concepts of Liberty" (value pluralism)
3. Amy Gutmann & Dennis Thompson - "Democracy and Disagreement" (deliberative democracy)
4. Ruth Chang - "Incommensurability, Incomparability, and Practical Reason" (1997)
5. Iris Marion Young - "Inclusion and Democracy" (2000)
6. NYC Local Law 144 (Automated Employment Decision Tools, 2023)
7. EU AI Act (2024)
8. Google Trends, news coverage, academic literature (various sources)

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END OF DOCUMENT

Next: See Document 2 for deep-dive analysis of Algorithmic Hiring Transparency scenario