Alternatives and similar repositories for fairness-in-ml:

Users that are interested in fairness-in-ml are comparing it to the libraries listed below

• algofairness / fairness-comparison
  Comparing fairness-aware machine learning techniques.
  ☆160Updated 2 years ago
• mbilalzafar / fair-classification
  Python code for training fair logistic regression classifiers.
  ☆189Updated 3 years ago
• gpleiss / equalized_odds_and_calibration
  Code and data for the experiments in "On Fairness and Calibration"
  ☆50Updated 2 years ago
• steverab / failing-loudly
  Code repository for our paper "Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift": https://arxiv.org/abs/1810.119…
  ☆103Updated 11 months ago
• Jianbo-Lab / L2X
  ☆125Updated 3 years ago
• jphall663 / xai_misconceptions
  Preprint/draft article/blog on some explainable machine learning misconceptions. WIP!
  ☆28Updated 5 years ago
• MarcelRobeer / ContrastiveExplanation
  Contrastive Explanation (Foil Trees), developed at TNO/Utrecht University
  ☆45Updated 2 years ago
• dssg / fairness_tutorial
  Hands-on tutorial on ML Fairness
  ☆70Updated last year
• cosmicBboy / themis-ml
  A library that implements fairness-aware machine learning algorithms
  ☆125Updated 4 years ago
• fair-preprocessing / nips2017
  ☆26Updated 7 years ago
• dylan-slack / Fooling-LIME-SHAP
  Adversarial Attacks on Post Hoc Explanation Techniques (LIME/SHAP)
  ☆82Updated 2 years ago
• OscarcarLi / PrototypeDL
  Codebase for "Deep Learning for Case-based Reasoning through Prototypes: A Neural Network that Explains Its Predictions" (to appear in AA…
  ☆74Updated 7 years ago
• csinva / hierarchical-dnn-interpretations
  Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)
  ☆128Updated 3 years ago
• google / ml-fairness-gym
  ☆313Updated 2 years ago
• ustunb / actionable-recourse
  python tools to check recourse in linear classification
  ☆75Updated 4 years ago
• columbia / fairtest
  ☆58Updated 3 years ago
• d909b / cxplain
  Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.
  ☆130Updated 4 years ago
• BeenKim / MMD-critic
  ☆133Updated 5 years ago
• iancovert / removal-explanations
  A lightweight implementation of removal-based explanations for ML models.
  ☆59Updated 3 years ago
• andreysharapov / xaience
  All about explainable AI, algorithmic fairness and more
  ☆107Updated last year
• jmikko / fair_ERM
  Fair Empirical Risk Minimization (FERM)
  ☆37Updated 4 years ago
• google / TrustScore
  To Trust Or Not To Trust A Classifier. A measure of uncertainty for any trained (possibly black-box) classifier which is more effective t…
  ☆174Updated last year
• SinaMohseni / ML-Interpretability-Evaluation-Benchmark
  This is a benchmark to evaluate machine learning local explanaitons quality generated from any explainer for text and image data
  ☆30Updated 3 years ago
• suraz09 / FairClassifier
  An open source package that evaluate the fairness of a Neural Network using a fairness metric called p% rule.
  ☆18Updated 6 years ago
• GDPlumb / MAPLE
  Supervised Local Modeling for Interpretability
  ☆28Updated 6 years ago
• amiratag / DataShapley
  Data Shapley: Equitable Valuation of Data for Machine Learning
  ☆269Updated 10 months ago
• Kritikalcoder / DP-primer
  A concise primer on Differential Privacy
  ☆28Updated 4 years ago
• marcoancona / DASP
  Keras implementation for DASP: Deep Approximate Shapley Propagation (ICML 2019)
  ☆61Updated 5 years ago
• charmlab / mace
  Model Agnostic Counterfactual Explanations
  ☆87Updated 2 years ago
• BorealisAI / private-data-generation
  A toolbox for differentially private data generation
  ☆130Updated last year