Alternatives and similar repositories for Fooling-LIME-SHAP

Users that are interested in Fooling-LIME-SHAP are comparing it to the libraries listed below

• charmlab / mace
  Model Agnostic Counterfactual Explanations
  ☆88Updated 3 years ago
• mbilalzafar / fair-classification
  Python code for training fair logistic regression classifiers.
  ☆192Updated 3 years ago
• iancovert / removal-explanations
  A lightweight implementation of removal-based explanations for ML models.
  ☆58Updated 4 years ago
• BorealisAI / private-data-generation
  A toolbox for differentially private data generation
  ☆131Updated 2 years ago
• AI4LIFE-GROUP / OpenXAI
  OpenXAI : Towards a Transparent Evaluation of Model Explanations
  ☆248Updated last year
• SinaMohseni / Awesome-XAI-Evaluation
  Reference tables to introduce and organize evaluation methods and measures for explainable machine learning systems
  ☆75Updated 3 years ago
• algofairness / fairness-comparison
  Comparing fairness-aware machine learning techniques.
  ☆159Updated 2 years ago
• hbaniecki / adversarial-explainable-ai
  💡 Adversarial attacks on explanations and how to defend them
  ☆328Updated 10 months ago
• 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 4 years ago
• andreysharapov / xaience
  All about explainable AI, algorithmic fairness and more
  ☆110Updated 2 years ago
• abacusai / xai-bench
  XAI-Bench is a library for benchmarking feature attribution explainability techniques
  ☆70Updated 2 years ago
• socialfoundations / folktables
  Datasets derived from US census data
  ☆269Updated last year
• carla-recourse / CARLA
  CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
  ☆295Updated 2 years ago
• csinva / hierarchical-dnn-interpretations
  Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)
  ☆129Updated 4 years ago
• choprashweta / Adversarial-Debiasing
  Implementation of Adversarial Debiasing in PyTorch to address Gender Bias
  ☆31Updated 5 years ago
• divyat09 / cf-feasibility
  Code accompanying the paper "Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers"
  ☆31Updated 2 years ago
• fat-forensics / fat-forensics
  Modular Python Toolbox for Fairness, Accountability and Transparency Forensics
  ☆77Updated 2 years ago
• equialgo / fairness-in-ml
  This repository contains the full code for the "Towards fairness in machine learning with adversarial networks" blog post.
  ☆118Updated 4 years ago
• riccotti / LORE
  LOcal Rule-based Exlanations
  ☆53Updated last year
• datamllab / awesome-fairness-in-ai
  A curated list of awesome Fairness in AI resources
  ☆328Updated 2 years ago
• jmikko / fair_ERM
  Fair Empirical Risk Minimization (FERM)
  ☆37Updated 5 years ago
• ustunb / actionable-recourse
  python tools to check recourse in linear classification
  ☆78Updated 4 years ago
• IBM / Contrastive-Explanation-Method
  Codes for reproducing the contrastive explanation in “Explanations based on the Missing: Towards Contrastive Explanations with Pertinent…
  ☆54Updated 7 years ago
• rehmanzafar / xai-iml-sota
  Interesting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Human…
  ☆74Updated 3 years ago
• x-y-zhao / BayLime
  bayesian lime
  ☆18Updated last year
• Jianbo-Lab / L2X
  ☆124Updated 4 years ago
• google / ml-fairness-gym
  ☆316Updated 2 years ago
• steven7woo / fair_regression_reduction
  General fair regression subject to demographic parity constraint. Paper appeared in ICML 2019.
  ☆16Updated 5 years ago
• GDPlumb / MAPLE
  Supervised Local Modeling for Interpretability
  ☆29Updated 6 years ago
• BeenKim / MMD-critic
  ☆134Updated 6 years ago