A collection of research materials on explainable AI/ML

CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms

A collection of algorithms of counterfactual explanations.

A list of research papers of explainable machine learning.

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This is the official repository of the paper "CounterNet: End-to-End Training of Counterfactual Aware Predictions".

This repository contains the code and scripts to reproduce the results from our paper "HARE: Human-in-the-Loop Algorithmic Recourse", accepted at TMLR 2025.

GlassAlpha is an open-source toolkit for deterministic, regulator-ready ML audit reports. One command generates comprehensive, reproducible audits with TreeSHAP explainability, fairness metrics, calibration analysis, and robustness testing for XGBoost, LightGBM, and logistic regression models (binary classification, tabular data only).

Recourse Explanation Library in JAX

Banned from a site or organization? Account suspended? Censored? Why?

This project Implements the paper “Robustness implies Fairness in Casual Algorithmic Recourse” using the R language.

Counterfactual Explanations with Probabilistic Guarantees on their Robustness to Model Change (KDD'25)

Code associated with "Recourse For Humans", presented at the Participatory Approaches to Machine Learning workshop at ICML 2020.

Python implementation of the work "The importance of Time in Causal Algorithmic Recourse"

This repo contains weekly notes, tools cheatsheets, malware reports, YARA rules, and resources to help you systematically master malware analysis from foundational concepts to advanced research and career preparation.

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This is the official repository of the paper "RoCourseNet: Distributionally Robust Training of a Prediction Aware Recourse Model".