Alternatives and similar repositories for equalized_odds_and_calibration

Users that are interested in equalized_odds_and_calibration are comparing it to the libraries listed below

• Jianbo-Lab / L2X
  ☆125Updated 4 years ago
• equialgo / fairness-in-ml
  This repository contains the full code for the "Towards fairness in machine learning with adversarial networks" blog post.
  ☆119Updated 4 years ago
• BeenKim / MMD-critic
  ☆135Updated 6 years ago
• ustunb / actionable-recourse
  python tools to check recourse in linear classification
  ☆77Updated 5 years 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
• dtak / rrr
  Code/figures in Right for the Right Reasons
  ☆57Updated 5 years ago
• algofairness / fairness-comparison
  Comparing fairness-aware machine learning techniques.
  ☆160Updated 3 years ago
• csinva / hierarchical-dnn-interpretations
  Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)
  ☆129Updated 4 years ago
• debmandal / causality-fairness
  References for Papers at the Intersection of Causality and Fairness
  ☆18Updated 7 years ago
• nikikilbertus / blind-justice
  Blind Justice Code for the paper "Blind Justice: Fairness with Encrypted Sensitive Attributes", ICML 2018
  ☆14Updated 6 years ago
• mbilalzafar / fair-classification
  Python code for training fair logistic regression classifiers.
  ☆192Updated 4 years ago
• namkoong-lab / robustopt
  ☆43Updated 7 years ago
• charmlab / mace
  Model Agnostic Counterfactual Explanations
  ☆88Updated 3 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…
  ☆76Updated 8 years ago
• vveitch / causal-network-embeddings
  Software and pre-processed data for "Using Embeddings to Correct for Unobserved Confounding in Networks"
  ☆57Updated 2 years ago
• marcotcr / anchor-experiments
  Experiments for AAAI anchor paper
  ☆66Updated 7 years ago
• blei-lab / deconfounder_tutorial
  ☆87Updated 5 years ago
• blei-lab / causal-text-embeddings
  Software and data for "Using Text Embeddings for Causal Inference"
  ☆126Updated 5 years ago
• GDPlumb / MAPLE
  Supervised Local Modeling for Interpretability
  ☆29Updated 7 years ago
• d909b / cxplain
  Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.
  ☆132Updated 5 years ago
• fair-preprocessing / nips2017
  ☆26Updated 8 years ago
• JonathanWenger / pycalib
  Non-Parametric Calibration for Classification (AISTATS 2020)
  ☆19Updated 3 years ago
• amiratag / InterpretationFragility
  Interpretation of Neural Network is Fragile
  ☆36Updated last year
• nke001 / causal_learning_unknown_interventions
  Code for "Neural causal learning from unknown interventions"
  ☆104Updated 5 years ago
• SeojinBang / VIBI
  Explaining a black-box using Deep Variational Information Bottleneck Approach
  ☆46Updated 3 years ago
• rsheth80 / pmf-automl
  Data and code related to the paper "Probabilistic matrix factorization for automated machine learning", NIPS, 2018.
  ☆40Updated 4 years ago
• iancovert / removal-explanations
  A lightweight implementation of removal-based explanations for ML models.
  ☆59Updated 4 years ago
• Piyushi-0 / ACE
  Code for our ICML '19 paper: Neural Network Attributions: A Causal Perspective.
  ☆51Updated 4 years ago
• lydiatliu / delayedimpact
  Code for reproducing results in Delayed Impact of Fair Machine Learning (Liu et al 2018)
  ☆14Updated 3 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…
  ☆177Updated 2 years ago