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Fair Code - Public Roadmap

This is the public roadmap for Fair Code. It tracks what has been built, what is actively in progress, and what comes next.

Last updated: June 2026

Where We Are

Fair Code is an open-source responsible AI platform explaining algorithmic bias, fairness, and AI accountability through code audits, explainers, healthcare-bias case studies, and contributor-led GitHub documentation.

Current traction (June 2026):

  • 27+ GitHub stars
  • 7+ external contributors
  • 8+ forks
  • 10K+ combined social reach (Instagram + LinkedIn)
  • 6 published code audits
  • 22 published explainers
  • CI pipeline running on every push and PR

Phase 1 - Bias Glossary and Beginner Explainers ✅

Status: Complete

Build the foundational vocabulary and explain core fairness concepts clearly enough for a non-technical reader.

  • Proxy Variables
  • Equalized Odds
  • Sampling Bias
  • SHAP Values
  • Disparate Impact (The 80% Rule)
  • Disparate Treatment
  • Why Fairness Metrics Conflict
  • Calibration
  • Demographic Parity
  • Feedback Loop Bias
  • Label Bias
  • Individual Fairness
  • Counterfactual Fairness
  • What Happens Inside a Neural Network
  • Why AI Hallucinates
  • What Is Reinforcement Learning
  • Proxy Entanglement
  • What Is Machine Learning Bias
  • What Is Data Leakage
  • How AI Detects Patterns
  • What Is Distribution Shift
  • The Biggest Myth About AI Objectivity
  • What Is a Confounding Variable?

Phase 2 - Healthcare AI Bias Examples ✅ / 🔄 In Progress

Status: Audits complete - explainers expanding

Publish healthcare-specific bias audits and explainers that show how AI discrimination shows up in clinical and insurance contexts.

  • Insurance Denial bias audit
  • Benefits Denial bias audit
  • Healthcare Readmission bias audit
  • Jupyter notebooks for all three healthcare audits
  • Explainer: Why Accuracy Is Not Enough in Healthcare AI
  • Explainer: False Positives and False Negatives in Medical Risk Models
  • Case study write-up: Insurance Denial Bias (standalone explainer page)
  • Case study write-up: Benefits Denial Bias (standalone explainer page)
  • Case study write-up: Healthcare Readmission Bias (standalone explainer page)

Phase 3 - Code Audits 🔄 In Progress

Status: 6 of 8 planned audits published

Each audit follows the same pipeline: train a biased model → measure the fairness gap → remove proxies → retrain → measure again.

  • COMPAS - Criminal Justice Bias
  • AI Fair Recruitment - Hiring Bias
  • German Credit Lending - Lending Bias
  • Insurance Denial - Healthcare Bias
  • Benefits Denial - Welfare Eligibility Bias
  • Healthcare Readmission - Clinical Bias
  • HMDA Mortgage Lending Bias
  • Facial Recognition Accuracy Gaps (MIT Gender Shades methodology)
  • LLM bias audit

Phase 4 - Contributor Expansion 🔄 In Progress

Status: Active - 7 external contributors

Goal: grow to 10+ contributors with quality-controlled contributions.

  • CONTRIBUTING.md
  • Issue templates (bug report, new audit, new explainer)
  • PR template
  • CODE_OF_CONDUCT.md
  • CI pipeline (all audit scripts run on push/PR)
  • Good-first-issue and help-wanted labels
  • First-interaction workflow (greets new contributors)
  • 10–15 labelled issues open at all times
  • Contributor list in README
  • METRICS.md tracking contributor growth weekly

Phase 5 - Fairness Metrics and Notebooks ⏳ Planned

Status: Planned

Go deeper on measurement - fairness dashboards, interactive notebooks, and statistical tools for auditors.

  • Fairness audit web dashboard - Open Dataset Profiler (profiler.html)
  • Bias detection utility library (faircode/ module) - diagnostic representation profiler + CLI
  • AIF360 / Fairlearn integration examples
  • Intersectional bias notebook (auditing across multiple protected attributes simultaneously)
  • Statistical significance testing for fairness gaps

Content Schedule

During school:

  • Monday: AI bias explainer
  • Wednesday: Healthcare AI / fairness example
  • Friday: Code audit or project update

During holidays:

  • Monday–Friday posting acceptable if sustainable

How to Contribute

See CONTRIBUTING.md to claim an open issue or propose a new audit or explainer.

If you want to take on a Phase 3 audit (HMDA, facial recognition, or LLM bias), open an issue first with a brief description of your approach and the dataset you plan to use.

Fair Code is maintained by Yash Kewlani. Follow the project at @thefaircodeproject.