docs/roadmap.md

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# ExFairness Implementation Roadmap

## Vision

ExFairness aims to be the definitive fairness and bias detection library for the Elixir ML ecosystem, providing production-ready tools for building equitable AI systems.

## Phases

### Phase 1: Core Metrics (v0.1.0) - Foundation

**Goal**: Establish core fairness metrics infrastructure

**Deliverables**:

1. **Basic Infrastructure**
   - [x] Project setup with mix
   - [x] Documentation structure
   - [x] Architecture design
   - [ ] Core module structure
   - [ ] Nx integration

2. **Group Fairness Metrics**
   - [ ] Demographic Parity
     - Basic computation
     - Statistical testing
     - Confidence intervals
   - [ ] Equalized Odds
     - TPR/FPR computation
     - Confusion matrix utilities
   - [ ] Equal Opportunity
     - TPR computation
     - Interpretation utilities
   - [ ] Predictive Parity
     - PPV/NPV computation

3. **Testing & Documentation**
   - [ ] Unit tests for all metrics
   - [ ] Property-based tests
   - [ ] Usage examples
   - [ ] API documentation

**Timeline**: 4-6 weeks

---

### Phase 2: Detection & Reporting (v0.2.0) - Analysis

**Goal**: Comprehensive bias detection and reporting capabilities

**Deliverables**:

1. **Bias Detection**
   - [ ] Disparate Impact Analysis
     - 80% rule implementation
     - Statistical significance testing
   - [ ] Statistical Parity Testing
     - Chi-square tests
     - Permutation tests
   - [ ] Intersectional Analysis
     - Multi-attribute combinations
     - Subgroup discovery
   - [ ] Label Bias Detection
     - Distribution analysis
     - Similarity-based detection

2. **Reporting System**
   - [ ] Fairness Report Generation
     - Multi-metric aggregation
     - Interpretation engine
     - Recommendations
   - [ ] Export Formats
     - Markdown
     - JSON
     - HTML
   - [ ] Visualization Support
     - Metric plots
     - Disparity heatmaps

3. **Temporal Monitoring**
   - [ ] Drift Detection
     - CUSUM implementation
     - EWMA charts
   - [ ] Time-series utilities
   - [ ] Alert system

**Timeline**: 6-8 weeks

---

### Phase 3: Mitigation (v0.3.0) - Action

**Goal**: Practical bias mitigation techniques

**Deliverables**:

1. **Pre-processing Methods**
   - [ ] Reweighting
     - Demographic parity weights
     - Equalized odds weights
   - [ ] Resampling
     - Oversampling minority groups
     - Undersampling majority groups
   - [ ] Fair Representation Learning
     - VAE-based approach
     - MMD independence

2. **Post-processing Methods**
   - [ ] Threshold Optimization
     - Grid search
     - Gradient-based optimization
     - Pareto frontier analysis
   - [ ] Calibration
     - Platt scaling per group
     - Isotonic regression
   - [ ] Reject Option Classification
     - Uncertainty-based rejection

3. **In-processing Methods** (Axon Integration)
   - [ ] Adversarial Debiasing
     - Predictor-adversary architecture
     - Training loop
   - [ ] Fairness Constraints
     - Lagrangian optimization
     - Penalty methods

**Timeline**: 8-10 weeks

---

### Phase 4: Advanced Metrics (v0.4.0) - Research

**Goal**: State-of-the-art fairness metrics

**Deliverables**:

1. **Individual Fairness**
   - [ ] Lipschitz Fairness
     - Similarity metrics
     - Consistency checking
   - [ ] Metric Learning
     - Learn fair distance metrics

2. **Causal Fairness**
   - [ ] Counterfactual Fairness
     - Causal graph specification
     - Counterfactual generation
   - [ ] Path-Specific Effects
     - Direct/indirect discrimination
   - [ ] Mediation Analysis

3. **Calibration Metrics**
   - [ ] Multi-calibration
     - Calibration across subgroups
   - [ ] Expected Calibration Error
   - [ ] Reliability Diagrams

4. **Additional Metrics**
   - [ ] Fairness Through Unawareness
   - [ ] Treatment Equality
   - [ ] Test Fairness (Conditional Use Accuracy Equality)

**Timeline**: 10-12 weeks

---

### Phase 5: Production Tools (v0.5.0) - Scale

**Goal**: Production-ready monitoring and deployment tools

**Deliverables**:

1. **Monitoring System**
   - [ ] Real-time Fairness Monitoring
     - Streaming metrics computation
     - Online drift detection
   - [ ] Dashboard Integration
     - LiveView dashboard
     - Metrics visualization
   - [ ] Alert System
     - Configurable thresholds
     - Notification integration

2. **Audit & Compliance**
   - [ ] Audit Trail
     - Fairness assessments logging
     - Decision tracking
   - [ ] Compliance Reports
     - EEOC compliance
     - EU AI Act
     - GDPR considerations

3. **Performance Optimization**
   - [ ] EXLA Backend Support
     - GPU acceleration
     - Distributed computation
   - [ ] Caching System
     - Metric caching
     - Result memoization
   - [ ] Benchmarking Suite

4. **Integration**
   - [ ] Scholar Integration
     - Fairness wrappers for ML models
   - [ ] Bumblebee Integration
     - LLM fairness assessment
   - [ ] Explorer Integration
     - DataFrame-based API

**Timeline**: 12-14 weeks

---

### Phase 6: Ecosystem & Extensions (v1.0.0) - Maturity

**Goal**: Comprehensive ecosystem and community

**Deliverables**:

1. **Domain-Specific Tools**
   - [ ] NLP Fairness
     - Text bias detection
     - Language model fairness
   - [ ] Computer Vision Fairness
     - Image bias detection
     - Face recognition fairness
   - [ ] Recommender System Fairness
     - Exposure fairness
     - Recommendation diversity

2. **AutoML Integration**
   - [ ] Fairness-Aware Hyperparameter Tuning
   - [ ] Multi-objective Optimization
     - Accuracy-fairness Pareto optimization
   - [ ] Model Selection
     - Fair model ranking

3. **Educational Resources**
   - [ ] Interactive Tutorials
   - [ ] Case Studies
     - Lending
     - Hiring
     - Healthcare
     - Criminal justice
   - [ ] Best Practices Guide
   - [ ] Video Tutorials

4. **Community & Governance**
   - [ ] Contribution Guidelines
   - [ ] Code of Conduct
   - [ ] Governance Model
   - [ ] Community Forum

**Timeline**: Ongoing

---

## Technical Milestones

### Milestone 1: MVP (End of Phase 1)
- Core metrics working
- Basic documentation
- Initial Hex release

### Milestone 2: Production Beta (End of Phase 3)
- Full metric suite
- Mitigation techniques
- Production-ready documentation

### Milestone 3: v1.0 Release (End of Phase 6)
- Complete feature set
- Comprehensive documentation
- Production deployments

---

## Research Priorities

### Short-term (6 months)
1. Implement core impossibility theorem demonstrations
2. Add support for multi-class fairness
3. Develop fairness-accuracy tradeoff analysis

### Medium-term (12 months)
1. Causal fairness implementation
2. Fairness in federated learning
3. Fairness for generative models

### Long-term (18+ months)
1. Fairness in reinforcement learning
2. Dynamic fairness (fairness over time)
3. Fairness in multi-agent systems

---

## Community Engagement

### Documentation
- [ ] Comprehensive API docs
- [ ] Tutorial series
- [ ] Blog posts
- [ ] Conference talks
- [ ] Academic papers

### Outreach
- [ ] ElixirConf presentation
- [ ] Academic collaborations
- [ ] Industry partnerships
- [ ] Open-source sprints

---

## Success Metrics

### Adoption
- 1000+ hex downloads in first 6 months
- 100+ GitHub stars in first year
- 10+ production deployments

### Quality
- 90%+ test coverage
- < 5 critical bugs per release
- < 1 week median issue resolution time

### Community
- 20+ contributors
- 50+ community discussions
- 5+ third-party integrations

---

## Dependencies & Integration

### Core Dependencies
- **Nx**: Numerical computing (existing)
- **EXLA**: GPU acceleration (planned)
- **Statistex**: Statistical tests (optional)

### Integration Targets
- **Axon**: Neural network training
- **Scholar**: Classical ML algorithms
- **Bumblebee**: LLM evaluation
- **Explorer**: Data manipulation
- **VegaLite**: Visualization

---

## Risk Assessment

### Technical Risks
1. **Performance**: Large-scale fairness computation may be slow
   - Mitigation: GPU acceleration, sampling strategies

2. **Numerical Stability**: Some metrics may be numerically unstable
   - Mitigation: Careful numerical implementation, validation tests

3. **API Design**: API may need breaking changes
   - Mitigation: Careful design review, user feedback

### Ecosystem Risks
1. **Adoption**: Limited Elixir ML ecosystem
   - Mitigation: Cross-promote with other North Shore AI projects

2. **Maintenance**: Sustainability of open-source project
   - Mitigation: Clear governance, contributor onboarding

---

## Release Strategy

### Versioning
- Semantic versioning (MAJOR.MINOR.PATCH)
- Pre-1.0: Breaking changes allowed in MINOR versions
- Post-1.0: Breaking changes only in MAJOR versions

### Release Cadence
- Phase 1-3: Monthly releases
- Phase 4-6: Bi-monthly releases
- Post-1.0: Quarterly releases

### Communication
- Release notes on GitHub
- Blog posts for major releases
- Hex.pm package updates
- Social media announcements

---

## Long-term Vision (2+ years)

1. **Standard Library**: ExFairness becomes the de-facto fairness library for Elixir ML
2. **Research Impact**: Published papers citing ExFairness
3. **Industry Impact**: Production deployments in Fortune 500 companies
4. **Regulatory Impact**: Referenced in fairness compliance frameworks
5. **Educational Impact**: Used in university ML courses

---

## Contributing

See [CONTRIBUTING.md](../CONTRIBUTING.md) for details on:
- Setting up development environment
- Code style guidelines
- Testing requirements
- Pull request process
- Issue triage

---

## Changelog

Major changes will be documented in [CHANGELOG.md](../CHANGELOG.md)

---

## Next Steps

**Immediate (Next 2 weeks)**:
1. Implement `ExFairness` main module
2. Implement `ExFairness.Metrics.DemographicParity`
3. Set up test infrastructure
4. Create usage examples

**Short-term (Next month)**:
1. Complete Phase 1 deliverables
2. Initial Hex release
3. Documentation site setup

**Medium-term (Next quarter)**:
1. Complete Phase 2 deliverables
2. Community outreach
3. First production deployment

---

*Last Updated: 2025-10-10*