summerscope / mapping-fair-ml
πA curated list of links and resources for Fair ML and Data Ethics
β18Updated 2 years ago
Related projects: β
- β17Updated 4 years ago
- (ICML 2021) Mandoline: Model Evaluation under Distribution Shiftβ30Updated 3 years ago
- Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning (AISTATS 2022 Oral)β38Updated last year
- Official implementation of the paper "Interventions, Where and How? Experimental Design for Causal Models at Scale", NeurIPS 2022.β19Updated last year
- ModelDiff: A Framework for Comparing Learning Algorithmsβ52Updated last year
- Interactive Weak Supervision: Learning Useful Heuristics for Data Labelingβ30Updated 3 years ago
- Measuring data importance over ML pipelines using the Shapley value.β35Updated this week
- Interpretable and efficient predictors using pre-trained language models. Scikit-learn compatible.β37Updated 5 months ago
- Code for the anonymous submission "Cockpit: A Practical Debugging Tool for Training Deep Neural Networks"β31Updated 3 years ago
- Reading history for Fair ML Reading Group in Melbourneβ37Updated 3 years ago
- Testing Language Models for Memorization of Tabular Datasets.β26Updated last week
- A collection of implementations of fair ML algorithmsβ11Updated 6 years ago
- β18Updated 2 years ago
- The Medkit-Learn(ing) Environment: Medical Decision Modelling through Simulation (NeurIPS 2021) by Alex J. Chan, Ioana Bica, Alihan Huyukβ¦β27Updated 2 years ago
- A python library to discover and mitigate biases in machine learning models and datasetsβ20Updated last year
- Experiments for the NeurIPS 2021 paper "Cockpit: A Practical Debugging Tool for the Training of Deep Neural Networks"β13Updated 2 years ago
- Code for Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confoundingβ21Updated last year
- Notebooks for managing NeurIPS 2014 and analysing the NeurIPS experiment.β11Updated 3 months ago
- The Concept Bottleneck Shift Detection (CBSD) methods for explaining and detecting various dataset shifts.β14Updated 3 years ago
- PyTorch package to train and audit ML models for Individual Fairnessβ63Updated last year
- A lightweight implementation of removal-based explanations for ML models.β56Updated 3 years ago
- Hyperparameter tuning via uncertainty modelingβ46Updated 4 months ago
- AutoML Two-Sample Testβ18Updated 2 years ago
- Using / reproducing DAC from the paper "Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees"β25Updated 3 years ago
- Code and data for decision making under strategic behavior, NeurIPS 2020 & Management Science 2024.β26Updated 6 months ago
- The Recognizing, Exploring, and Articulating Limitations in Machine Learning research tool (REAL ML) is a set of guided activities to helβ¦β50Updated 2 years ago
- Code to reproduce our paper on probabilistic algorithmic recourse: https://arxiv.org/abs/2006.06831β34Updated last year
- A Natural Language Interface to Explainable Boosting Machinesβ59Updated 2 months ago
- β29Updated 5 years ago
- CEML - Counterfactuals for Explaining Machine Learning models - A Python toolboxβ42Updated last month