wbawakate / fairtorch
PyTorch implementation of parity loss as constraints function to realize the fairness of machine learning.
☆73Updated last year
Alternatives and similar repositories for fairtorch:
Users that are interested in fairtorch are comparing it to the libraries listed below
- Python codes for influential instance estimation☆55Updated 2 years ago
- code release for Representer point Selection for Explaining Deep Neural Network in NeurIPS 2018☆67Updated 3 years ago
- Combating hidden stratification with GEORGE☆63Updated 3 years ago
- Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)☆128Updated 3 years ago
- (ICML 2021) Mandoline: Model Evaluation under Distribution Shift☆31Updated 3 years ago
- Reference tables to introduce and organize evaluation methods and measures for explainable machine learning systems☆74Updated 3 years ago
- ☆134Updated 5 years ago
- Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" ht…☆127Updated 4 years ago
- ☆51Updated 4 years ago
- This is a benchmark to evaluate machine learning local explanaitons quality generated from any explainer for text and image data☆30Updated 3 years ago
- Towards Automatic Concept-based Explanations☆159Updated 11 months ago
- ☆109Updated 2 years ago
- REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets --- https://arxiv.org/abs/2004.07999☆110Updated 2 years ago
- Model Patching: Closing the Subgroup Performance Gap with Data Augmentation☆42Updated 4 years ago
- Code repository for our paper "Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift": https://arxiv.org/abs/1810.119…☆104Updated last year
- ☆125Updated 3 years ago
- This repository contains the full code for the "Towards fairness in machine learning with adversarial networks" blog post.☆117Updated 4 years ago
- Codebase for "Deep Learning for Case-based Reasoning through Prototypes: A Neural Network that Explains Its Predictions" (to appear in AA…☆75Updated 7 years ago
- This code reproduces the results of the paper, "Measuring Data Leakage in Machine-Learning Models with Fisher Information"☆50Updated 3 years ago
- Code for "Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?"☆46Updated last year
- Library implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI☆54Updated 2 years ago
- Figures & code from the paper "Shortcut Learning in Deep Neural Networks" (Nature Machine Intelligence 2020)☆97Updated 2 years ago
- Algorithms for abstention, calibration and domain adaptation to label shift.☆36Updated 4 years ago
- Code for the CVPR 2019 paper : Spectral Metric for Dataset Complexity Assessment☆45Updated last year
- A pytorch implementation of our jacobian regularizer to encourage learning representations more robust to input perturbations.☆125Updated last year
- Training and evaluating NBM and SPAM for interpretable machine learning.☆77Updated 2 years ago
- ☆31Updated 3 years ago
- Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.☆130Updated 4 years ago
- Code for paper [Explaining image classifiers by removing input features using generative models] [ACCV 2020] https://arxiv.org/abs/1910.0…☆15Updated 2 years ago
- Interpretation of Neural Network is Fragile☆36Updated 11 months ago