CITATION.cff

cff-version: 1.2.0
message: "If you use this project in your research, please cite it as below."
type: software
title: "Fair Code - Algorithmic Bias Detection & Mitigation"
abstract: "An open-source research project that exposes and fixes bias in
  real-world AI systems across criminal justice, hiring, lending, healthcare,
  welfare eligibility, and tenant screening. Each audit trains a biased model, measures the
  fairness gap, removes protected attributes and proxy variables, and proves
  the reduction with reproducible code and public datasets. A cross-domain
  benchmark harness applies one uniform pipeline to every audit - five
  mitigation strategies (including Fairlearn's ExponentiatedGradient
  in-processing and ThresholdOptimizer post-processing) across three model
  families, scored on six fairness metrics with bootstrap confidence
  intervals and permutation tests. Includes the Open Dataset Profiler (CLI
  and client-side web tool) for auditing any tabular dataset's demographic
  representation before a model is trained, and intersectional analysis that
  measures the compounded gap on the group at the intersection of two
  protected attributes."
authors:
  - family-names: Kewlani
    given-names: Yash
    alias: yakew7
    website: "https://github.com/yakew7"
repository-code: "https://github.com/yakew7/Fair-Code"
url: "https://www.thefaircode.xyz"
license: MIT
version: "2.0.0"
date-released: "2026-07-23"
keywords:
  - algorithmic-fairness
  - bias-detection
  - machine-learning
  - ai-ethics
  - proxy-variables
  - demographic-parity
  - intersectionality
  - dataset-profiling
  - compas
  - python