rehmanzafar / xai-iml-sota
Interesting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Human in Loop and Visual Analytics.
☆72Updated 2 years ago
Related projects ⓘ
Alternatives and complementary repositories for xai-iml-sota
- All about explainable AI, algorithmic fairness and more☆107Updated last year
- Reference tables to introduce and organize evaluation methods and measures for explainable machine learning systems☆73Updated 2 years ago
- Adversarial Attacks on Post Hoc Explanation Techniques (LIME/SHAP)☆80Updated last year
- OpenXAI : Towards a Transparent Evaluation of Model Explanations☆232Updated 3 months ago
- Model Agnostic Counterfactual Explanations☆87Updated 2 years ago
- Code and documentation for experiments in the TreeExplainer paper☆179Updated 5 years ago
- bayesian lime☆16Updated 3 months ago
- ☆33Updated 4 months ago
- Local explanations with uncertainty 💐!☆39Updated last year
- Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)☆125Updated 3 years ago
- CEML - Counterfactuals for Explaining Machine Learning models - A Python toolbox☆42Updated 3 months ago
- Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" ht…☆127Updated 3 years ago
- Code repository for our paper "Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift": https://arxiv.org/abs/1810.119…☆102Updated 7 months ago
- XAI-Bench is a library for benchmarking feature attribution explainability techniques☆57Updated last year
- This repository is all about papers and tools of Explainable AI☆36Updated 4 years ago
- Meaningful Local Explanation for Machine Learning Models☆41Updated last year
- 💡 Adversarial attacks on explanations and how to defend them☆299Updated 8 months ago
- Data-SUITE: Data-centric identification of in-distribution incongruous examples (ICML 2022)☆9Updated last year
- For calculating global feature importance using Shapley values.☆253Updated this week
- A lightweight implementation of removal-based explanations for ML models.☆57Updated 3 years ago
- Multi-Objective Counterfactuals☆40Updated 2 years ago
- Neural Additive Models (Google Research)☆26Updated 6 months ago
- Contrastive Explanation (Foil Trees), developed at TNO/Utrecht University☆45Updated last year
- Reliability diagrams visualize whether a classifier model needs calibration☆137Updated 2 years ago
- CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms☆283Updated last year
- Papers and code of Explainable AI esp. w.r.t. Image classificiation☆196Updated 2 years ago
- Python package for tackling multi-class imbalance problems. http://www.cs.put.poznan.pl/mlango/publications/multiimbalance/☆77Updated 6 months ago
- Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.☆129Updated 4 years ago
- Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations☆558Updated last week
- Extended Complexity Library in R☆57Updated 3 years ago