choprashweta / Adversarial-Debiasing
Implementation of Adversarial Debiasing in PyTorch to address Gender Bias
☆31Updated 4 years ago
Alternatives and similar repositories for Adversarial-Debiasing:
Users that are interested in Adversarial-Debiasing are comparing it to the libraries listed below
- General fair regression subject to demographic parity constraint. Paper appeared in ICML 2019.☆15Updated 4 years ago
- ☆12Updated 4 years ago
- ☆37Updated 2 years ago
- Reference tables to introduce and organize evaluation methods and measures for explainable machine learning systems☆74Updated 3 years ago
- Code for Environment Inference for Invariant Learning (ICML 2021 Paper)☆50Updated 3 years ago
- This is a collection of papers and other resources related to fairness.☆92Updated last year
- This is a benchmark to evaluate machine learning local explanaitons quality generated from any explainer for text and image data☆30Updated 3 years ago
- ☆38Updated 3 years ago
- code release for the paper "On Completeness-aware Concept-Based Explanations in Deep Neural Networks"☆53Updated 3 years ago
- ☆26Updated 7 years ago
- ☆22Updated 5 years ago
- A reproduced PyTorch implementation of the Adversarially Reweighted Learning (ARL) model, originally presented in "Fairness without Demog…☆20Updated 4 years ago
- Code and data for the experiments in "On Fairness and Calibration"☆51Updated 3 years ago
- Understanding Rare Spurious Correlations in Neural Network☆12Updated 2 years ago
- ☆89Updated last week
- Code for the CVPR 2021 paper: Understanding Failures of Deep Networks via Robust Feature Extraction☆35Updated 2 years ago
- Python implementation for evaluating explanations presented in "On the (In)fidelity and Sensitivity for Explanations" in NeurIPS 2019 for…☆25Updated 3 years ago
- Code for paper: Are Large Language Models Post Hoc Explainers?☆31Updated 9 months ago
- ☆14Updated 5 years ago
- Blind Justice Code for the paper "Blind Justice: Fairness with Encrypted Sensitive Attributes", ICML 2018☆14Updated 6 years ago
- Code for "Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?"☆46Updated last year
- Code to reproduce our paper on probabilistic algorithmic recourse: https://arxiv.org/abs/2006.06831☆36Updated 2 years ago
- Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning (AISTATS 2022 Oral)☆40Updated 2 years ago
- Simple data balancing baselines for worst-group-accuracy benchmarks.☆42Updated last year
- ☆22Updated 6 years ago
- Hands-on tutorial on ML Fairness☆71Updated last year
- Code for "Just Train Twice: Improving Group Robustness without Training Group Information"☆71Updated 11 months ago
- Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)☆128Updated 3 years ago
- Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Value (ICML 2023)☆18Updated last year
- Fair Empirical Risk Minimization (FERM)☆37Updated 4 years ago