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# Product Backlog & Feature Roadmap

This page tracks planned features, enhancements, and research directions for Open Navigator. Items are organized by theme and priority.

## 🎯 High Priority Features

### Politician Personality Profiles

**Status:** Planned  
**Reference:** [Personality Politics - Joe Biden Example](http://personality-politics.org/joe-biden-2024)

Create comprehensive personality and behavioral profiles for elected officials based on:

- **Public Voting Records:** Analyze voting patterns, bill sponsorships, and legislative priorities
- **Public Statements:** Analyze speeches, press releases, social media, and meeting transcripts
- **Psychological Frameworks:** Apply Big Five personality traits, moral foundations, leadership styles
- **Communication Patterns:** Analyze rhetoric, framing strategies, and messaging consistency
- **Constituency Alignment:** Compare positions with constituent demographics and values

**Data Sources:**
- OpenStates API (voting records, bill sponsorships)
- Meeting transcripts from `events_text_ai` (local positions and rhetoric)
- Social media APIs (public statements)
- Campaign finance data (FEC API)
- Census data (constituency demographics)

**Output Format:**
```json
{
  "person_id": "ocd-person/12345",
  "full_name": "Jane Smith",
  "office": "City Council Member, District 3",
  "jurisdiction": "Boston, MA",
  "personality_profile": {
    "big_five": {
      "openness": 0.75,
      "conscientiousness": 0.82,
      "extraversion": 0.68,
      "agreeableness": 0.71,
      "neuroticism": 0.35
    },
    "moral_foundations": {
      "care_harm": 0.85,
      "fairness_cheating": 0.78,
      "loyalty_betrayal": 0.52,
      "authority_subversion": 0.48,
      "sanctity_degradation": 0.42,
      "liberty_oppression": 0.71
    },
    "leadership_style": "collaborative",
    "policy_priorities": ["housing", "education", "climate"],
    "communication_style": {
      "clarity_score": 0.82,
      "consistency_score": 0.76,
      "emotional_tone": "optimistic"
    }
  },
  "voting_analysis": {
    "total_votes": 247,
    "attendance_rate": 0.94,
    "party_loyalty_score": 0.68,
    "bipartisan_collaboration_score": 0.45,
    "key_issues": [
      {
        "issue": "affordable_housing",
        "vote_count": 32,
        "support_rate": 0.91,
        "alignment_with_platform": 0.95
      }
    ]
  },
  "constituent_alignment": {
    "demographic_match": 0.72,
    "policy_position_match": 0.68,
    "responsiveness_score": 0.81
  },
  "sources": [
    "OpenStates voting records",
    "City Council meeting transcripts",
    "Campaign website positions",
    "Social media analysis (Twitter/X, Facebook)"
  ],
  "last_updated": "2026-05-05"
}
```

**Implementation Steps:**
1. ✅ Create `bronze_contacts` table (DONE - already exists)
2. ✅ Extract people from meeting transcripts (DONE)
3. 🔲 Build personality analysis pipeline using LLMs
4. 🔲 Integrate voting record analysis from OpenStates
5. 🔲 Apply psychological frameworks (Big Five, Moral Foundations)
6. 🔲 Create profile aggregation and scoring system
7. 🔲 Build UI components for profile visualization
8. 🔲 Add profile comparison tools (compare candidates, track changes over time)

**Related Tables:**
- `bronze_contacts` - People extracted from meetings
- `opencivicdata_person` - Legislators from OpenStates
- `bronze_decisions` - Voting behavior in local meetings
- `bronze_organizations` - Organizational affiliations

---

## 📊 Data & Analytics Features

### Multi-Model AI Evaluation Dashboard

**Status:** In Progress (documentation complete, implementation pending)

Visual dashboard to compare AI model extractions and show consensus/contradictions.

**Features:**
- Side-by-side comparison of model outputs
- Consensus visualization (what all models agree on)
- Contradiction highlighting with explanation
- Quality metrics (Faithfulness, Relevancy, Coherence)
- Model performance tracking over time

**Reference:** [AI Model Evaluation](./ai-model-evaluation.md)

### Advanced Frame Analysis Aggregation

**Status:** Planned

Build aggregated views of policy frame analysis across:
- Time (how frames evolve)
- Geography (regional differences in framing)
- Issues (dominant frames per topic)
- Decision outcomes (which frames predict success/failure)

### Predictive Analytics

**Status:** Research Phase

Use historical meeting data to predict:
- Likely decision outcomes based on arguments presented
- Budget allocation patterns
- Policy adoption timelines
- Public engagement levels

---

## 🔍 Search & Discovery

### Semantic Search Across All Content

**Status:** Partially Implemented

Expand semantic search to cover:
- Meeting transcripts
- Decision statements
- Nonprofit descriptions
- Elected official statements
- Legislation text

### Advanced Filtering

**Status:** Planned

Multi-dimensional filtering:
- Geographic (state → county → city → district)
- Temporal (date ranges, meeting frequency)
- Thematic (COFOG codes, NTEE categories)
- Sentiment (supportive vs. opposed arguments)
- Financial impact (budget thresholds)

---

## 🤝 Collaboration & Engagement

### Constituent Communication Tools

**Status:** Planned

Help residents engage with elected officials:
- Template generator for public comments
- Meeting reminder notifications
- Issue tracking (follow specific topics)
- Elected official contact finder
- Public comment submission tracking

### Advocacy Campaign Builder

**Status:** Planned

Tools for organizers:
- Campaign strategy templates
- Target identification (key decision-makers)
- Power mapping visualization
- Coalition building tools
- Impact measurement dashboard

---

## 🧠 AI & Machine Learning

### Fine-Tuned Models for Civic Analysis

**Status:** Research Phase

Train domain-specific models:
- **Civic-BERT:** Fine-tuned for government meeting analysis
- **Policy-Frame-GPT:** Specialized in frame analysis
- **Vote-Predictor:** Predict council vote outcomes
- **Sentiment-Civic:** Sentiment analysis for public comments

### Ensemble Model Implementation

**Status:** In Progress

Implement production-ready ensemble pipelines:
- Automated MoA synthesis for all new meetings
- Multi-model comparison for quality assurance
- Confidence scoring for extracted facts
- Human-in-the-loop for low-confidence items

**Reference:** [AI Model Merging](./ai-model-merging.md)

---

## 🗺️ Geographic & Spatial Features

### Interactive Power Maps

**Status:** Planned

Visualize power dynamics from frame analysis:
- Stakeholder influence networks
- Constituent vs. developer interests
- Coalition formation patterns
- Decision-making pathways

### Geospatial Analysis

**Status:** Planned

Map-based views:
- Jurisdictions by policy topic heatmap
- Nonprofit density by issue area
- Meeting frequency by region
- Budget allocation patterns

---

## 📱 Platform & Infrastructure

### Mobile Application

**Status:** Planned

Native mobile apps for:
- Meeting notifications
- Live meeting viewing with AI summaries
- Quick contact lookup for elected officials
- Voice-to-text public comment submission

### Real-Time Meeting Analysis

**Status:** Research Phase

Live AI analysis during meetings:
- Real-time transcription and analysis
- Live frame detection
- Decision outcome prediction
- Instant fact-checking of claims

### Multi-Language Support

**Status:** Planned

Expand to support:
- Spanish (priority)
- Chinese
- Vietnamese
- Tagalog
- Other languages based on jurisdiction demographics

---

## 🔒 Privacy & Security

### Differential Privacy Implementation

**Status:** Research Phase

Protect individual privacy while enabling analysis:
- Anonymized voting patterns
- Aggregated demographic analysis
- Privacy-preserving personality profiles

### Data Governance Framework

**Status:** Planned

Implement comprehensive governance:
- Data retention policies
- Right to be forgotten mechanisms
- Consent management system
- Audit logging for all data access

---

## 🧪 Research & Experiments

### Causal Analysis of Policy Outcomes

**Status:** Research Phase

Use causal inference to understand:
- What policy interventions actually work
- Impact of framing on decision outcomes
- Effect of constituent engagement on votes
- Budget allocation effectiveness

### Comparative Jurisdiction Analysis

**Status:** Planned

Compare similar jurisdictions:
- Best practices identification
- Policy diffusion patterns
- Regional coordination opportunities
- Performance benchmarking

### LLM-Generated Policy Briefs

**Status:** Planned

Automatically generate:
- Meeting summaries for residents
- Policy impact analyses
- Comparison reports ("What did other cities do?")
- FAQ generation from meeting transcripts

---

## 🎨 User Experience

### Personalized Dashboard

**Status:** Planned

Customizable views based on user interests:
- "My Neighborhood" - local-only updates
- "My Issues" - filtered by policy topics
- "My Representatives" - track specific officials
- "My Meetings" - saved/bookmarked meetings

### Accessibility Improvements

**Status:** Ongoing

Enhance accessibility:
- Screen reader optimization
- Keyboard navigation
- High contrast themes
- Simplified language mode
- Audio descriptions for visualizations

---

## 📚 Documentation & Education

### Civic Education Curriculum

**Status:** Planned

Educational materials:
- "How Government Works" guides
- "Understanding Your Local Budget" tutorials
- "How to Make Public Comments" videos
- "Reading Meeting Minutes" guides

### API Documentation

**Status:** Partially Complete

Comprehensive developer docs:
- REST API reference
- GraphQL schema
- Authentication guides
- Rate limiting policies
- Code examples in multiple languages

---

## 🔗 Integration Goals

### Third-Party Integrations

**Status:** Planned

Connect with:
- **Civic platforms:** Participatory budgeting tools, petition platforms
- **Social media:** Auto-post meeting summaries
- **Calendar apps:** Meeting reminders
- **Communication tools:** Slack, Discord for community organizing
- **CRM systems:** For advocacy organizations

### Data Export & Portability

**Status:** Planned

Enable data export:
- CSV/Excel for offline analysis
- JSON API for programmatic access
- PDF reports for sharing
- Parquet files for data science
- Push to HuggingFace Datasets

---

## 💡 Community Requested Features

### Issue Tracking

**Status:** Planned

Track specific issues across time:
- "Follow this bill through the legislative process"
- "Alert me when budget item is discussed"
- "Track mentions of my neighborhood"

### Comparison Tools

**Status:** Planned

Compare across dimensions:
- Before/after policy implementation
- Your city vs. neighboring cities
- Current council vs. previous council
- Campaign promises vs. actual votes

### Public Comment Analysis

**Status:** Planned

Analyze public comments:
- Common themes in constituent feedback
- Sentiment trends over time
- Impact of public comment on votes
- Who speaks at meetings (demographics)

---

## 📅 Timeline (Rough Estimates)

### Q2 2026
- ✅ Multi-model comparison infrastructure (COMPLETE)
- ✅ MoA synthesis pipeline (COMPLETE)
- 🔲 Politician personality profiles (v1 prototype)
- 🔲 Multi-model evaluation dashboard

### Q3 2026
- 🔲 Fine-tuned civic models
- 🔲 Advanced filtering
- 🔲 Mobile app (beta)
- 🔲 Real-time meeting analysis (pilot)

### Q4 2026
- 🔲 Interactive power maps
- 🔲 Multi-language support (Spanish)
- 🔲 Constituent communication tools
- 🔲 API v2 launch

### 2027+
- 🔲 Predictive analytics
- 🔲 Causal analysis research
- 🔲 Civic education curriculum
- 🔲 Global expansion

---

## 🤝 How to Contribute

Have ideas for new features? Want to help implement something from this backlog?

1. **Comment on existing issues:** [GitHub Issues](https://github.com/getcommunityone/open-navigator/issues)
2. **Submit feature requests:** Use the "Feature Request" template
3. **Join discussions:** [GitHub Discussions](https://github.com/getcommunityone/open-navigator/discussions)
4. **Pick a backlog item:** Comment on the issue to claim it
5. **Submit a PR:** Reference the backlog item in your pull request

---

## 📊 Priority Framework

We prioritize features using:

**Impact:** How many users benefit? (🔴 High / 🟡 Medium / 🟢 Low)  
**Effort:** How much work is required? (🟢 Low / 🟡 Medium / 🔴 High)  
**Strategic:** Does it align with our mission? (⭐ Yes / - No)

**High Priority:** 🔴 Impact + 🟢 Low Effort + ⭐ Strategic  
**Medium Priority:** 🟡 Impact + 🟡 Effort + ⭐ Strategic  
**Research Phase:** 🔴 Impact + 🔴 High Effort (needs more investigation)

---

## 🎯 Feature Status Legend

- ✅ **Complete** - Feature is live and available
- 🔄 **In Progress** - Actively being developed
- 🔲 **Planned** - Committed to roadmap
- 🧪 **Research Phase** - Exploring feasibility
- 💭 **Idea** - Community suggestion, not yet scoped

---

*Last updated: May 5, 2026*  
*Next review: June 1, 2026*