values.md

Values.md Research Experiment: Do Personal Value Profiles Improve Human-AI Alignment?

Our comprehensive protocol for testing whether personalized values.md files enhance human-AI interaction quality, consistency, and alignment at personal scale.

The Values.md Research Experiment

Hypothesis

Primary Hypothesis: Personalized values.md files significantly improve human-AI interaction quality by providing LLMs with explicit ethical frameworks that align AI decision-making with individual moral reasoning patterns.

Secondary Hypotheses:

  1. LLMs with access to values.md will make decisions more consistent with user preferences than baseline prompting
  2. Decision quality (as measured by user satisfaction) will increase when LLMs reference personal ethical frameworks
  3. The effect will be measurable across different AI models and decision domains
  4. Values.md reduces the need for repeated clarification and context-setting in conversations

Research Protocol

Phase 1: Data Collection & Profile Generation

Participant Journey

  1. Ethical Dilemma Sequence: Participants complete 12 carefully designed ethical dilemmas spanning multiple domains:

    • Technology & Privacy
    • Medical & Healthcare
    • Workplace & Professional
    • Social & Community
    • Environmental & Sustainability

  2. Response Capture: For each dilemma, we collect:

    • Chosen option (A/B/C/D)
    • Written reasoning (optional but encouraged)
    • Perceived difficulty rating (1-10)
    • Response time (milliseconds)
    • Demographic context markers

  3. Values.md Generation: Our statistical analysis engine processes responses to generate personalized ethical profiles including:

    • Primary moral frameworks (utilitarian, deontological, virtue ethics, etc.)
    • Motif frequency analysis (UTIL_CALC, DUTY_CARE, HARM_MINIMIZE, etc.)
    • Consistency metrics and confidence scores
    • Decision pattern examples with reasoning
    • Explicit AI instruction formatting

Phase 2: Controlled AI Testing

Experimental Design

Control Group: LLMs receive standard prompts without values.md context Treatment Group: LLMs receive identical prompts + participant's values.md in system context

AI Model Battery

We test multiple LLM architectures to ensure findings generalize:

Decision Scenarios

Each AI model tackles identical sets of:

  1. Personal decision scenarios matching participant's dilemma domains
  2. Cross-domain ethical questions to test framework transfer
  3. Edge case dilemmas designed to reveal value conflicts
  4. Multi-stakeholder problems requiring complex moral reasoning

Phase 3: Assessment & Comparison

Quantitative Metrics

Qualitative Analysis

Methodology Implementation

Automated Experiment Infrastructure

Data Management

/experiments/
├── participants/           # Anonymous participant profiles
│   ├── {session-id}/
│   │   ├── responses.json     # Raw dilemma responses
│   │   ├── values.md          # Generated profile
│   │   ├── metadata.json     # Demographics, timestamps
│   │   └── ai_tests/         # Model comparison results
├── scenarios/             # Test scenario library
│   ├── personal/            # Tailored to participant domains
│   ├── cross_domain/        # Generalization tests
│   └── edge_cases/          # Value conflict scenarios
├── models/               # AI model configurations
└── results/              # Aggregated analysis data

Experimental Control System

Replicability Framework

Statistical Analysis Pipeline

Comparative Analysis

  1. Within-Participant: Compare control vs. treatment AI responses for same individual
  2. Between-Models: Assess which AI architectures benefit most from values.md
  3. Cross-Domain: Measure framework transfer effectiveness
  4. Longitudinal: Track consistency improvements over interaction history

Power Analysis & Sample Size

Data Privacy & Ethics

Privacy Protection

Research Ethics

Expected Outcomes & Impact

Validation Criteria

Success Metrics:

Broader Implications

  1. Personal AI Alignment: Evidence for scalable individual preference learning
  2. Ethical AI Development: Framework for incorporating human values in LLM training
  3. Human-Computer Interaction: New paradigm for context-aware AI systems
  4. Digital Ethics: Practical tool for value-aligned technology design

Experimental Status

Current Phase: Infrastructure Development & Pilot Testing Participant Recruitment: Q3 2025
Data Collection: Q4 2025 - Q1 2026 Analysis & Publication: Q2 2026

This research represents a novel approach to personalizing AI alignment at scale. By empirically testing whether explicit value profiles improve human-AI interaction, we aim to contribute both practical tools and theoretical insights to the growing field of AI ethics and alignment research.

Want to participate in our research? Join our study to contribute to the future of ethical AI.