clinicalml / teaching-to-understand-aiLinks
Code and webpages for our study on teaching humans to defer to an AI
☆11Updated last year
Alternatives and similar repositories for teaching-to-understand-ai
Users that are interested in teaching-to-understand-ai are comparing it to the libraries listed below
Sorting:
- Code for "Consistent Estimators for Learning to Defer to an Expert" (ICML 2020)☆13Updated 2 years ago
- Official Code Repo for the Paper: "How does This Interaction Affect Me? Interpretable Attribution for Feature Interactions", In NeurIPS 2…☆39Updated 2 years ago
- Code for ICLR 2020 paper: "Estimating counterfactual treatment outcomes over time through adversarially balanced representations" by I. B…☆59Updated last year
- Code/figures in Right for the Right Reasons☆55Updated 4 years ago
- A lightweight implementation of removal-based explanations for ML models.☆59Updated 3 years ago
- An Empirical Framework for Domain Generalization In Clinical Settings☆31Updated 3 years ago
- ☆10Updated 3 years ago
- Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning (AISTATS 2022 Oral)☆41Updated 2 years ago
- Neural Additive Models (Google Research)☆71Updated 3 years ago
- Efficient Computation and Analysis of Distributional Shapley Values (AISTATS 2021)☆21Updated last year
- Contains public materials for students enrolled in MITx: 6.871x, Machine Learning for Healthcare☆20Updated 4 years ago
- Code for "Generative causal explanations of black-box classifiers"☆34Updated 4 years ago
- This repository contains the implementation of Label-Free XAI, a new framework to adapt explanation methods to unsupervised models. For m…☆23Updated 2 years ago
- Library implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI☆55Updated 2 years ago
- Dataset containing 7,025 discharge summary notes from the MIMIC III dataset annotated for 7 SBDHs☆16Updated 3 years ago
- A collection of implementations of fair ML algorithms☆12Updated 7 years ago
- Code to reproduce our paper on probabilistic algorithmic recourse: https://arxiv.org/abs/2006.06831☆36Updated 2 years ago
- References for Papers at the Intersection of Causality and Fairness☆18Updated 6 years ago
- Approximate knockoffs and model-free variable selection.☆55Updated 3 years ago
- Code to study the generalisability of benchmark models on non-stationary EHRs.☆15Updated 5 years ago
- Codebase for "Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions", ICML 2020.☆9Updated 4 years ago
- A Python Package providing two algorithms, DAME and FLAME, for fast and interpretable treatment-control matches of categorical data☆59Updated last year
- Code and data for the experiments in "On Fairness and Calibration"☆51Updated 3 years ago
- This is the code for the paper Jacobian-based Causal Discovery with Nonlinear ICA, demonstrating how identifiable representations (partic…☆18Updated 10 months ago
- A python library to discover and mitigate biases in machine learning models and datasets☆20Updated 2 years ago
- Causal data augmentation for pretraining debiasing☆11Updated 3 years ago
- ☆23Updated last year
- Resources for Machine Learning Explainability☆80Updated 10 months ago
- Codebase for INVASE: Instance-wise Variable Selection - 2019 ICLR☆62Updated 5 years ago
- A distributed version of the sparse multi-output Gaussian process framework integrating python and C++.☆29Updated 7 years ago