ExFairness Implementation Roadmap

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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 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

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