(Free) Tools for Interpretable AI
- ELI5: Python package which helps to debug machine learning classifiers and explain their predictions
- InterpretML: open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof
- FAT Forensics : Python toolkit for evaluating Fairness, Accountability and Transparency of Artificial Intelligence systems
- AIX 360:
- 2 pages with links to different XAI projects:
https://awesomeopensource.com/projects/explainable-ai
https://github.com/jphall663/awesome-machine-learning-interpretability#python
Key venues in XAI and interesting talks
- XAI 2020: IJCAI workshop on Explainable AI (also done at IJCAI 2019)
- XKDD 2020: ECML-PKDD workshops on eXplainable Knowledge Discovery in Data Mining
- AIMLAI 2020 : CIKM workshop on Advances in Interpretable Machine Learning and Artificial Intelligence
- WHI 2020: ICML workshop on Human Interpretability in Machine Learning (5th edition, see also HILL 2019, WHI 2018, WHI 2017, WHI 2016).
- XXAI 2020: ICML workshop on Extending Explainable AI Beyond Deep Models and Classifiers
- CVPR 2020 Tutorial on Interpretable Machine Learning for Computer Vision (also done at ICCV’19, CVPR’18)
- VISxAI 2020: 3rd VIS workshop on Visualization for AI Explainability
- XKDD-AIMLAI 2019: ECML-PKDD joint workshops on Interpretable/Explainable AI
Key publications in XAI
- [LIME] Marco Túlio Ribeiro, Sameer Singh, Carlos Guestrin: “Why Should I Trust You?”: Explaining the Predictions of Any Classifier. KDD 2016: pp 1135-1144
- [SHAP] Scott M. Lundberg, Su-In Lee: A Unified Approach to Interpreting Model Predictions. NIPS 2017: pp 4768-4777
- [GRADCAM] R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-cam: Visual explanations from deep networks via gradient-based localization,” in IEEE International Conference on Computer Vision, ICCV, 2017, pp. 618–626.
- [Integrated Gradient] Mukund Sundararajan, Ankur Taly, Qiqi Yan:
Axiomatic Attribution for Deep Networks. ICML 2017: 3319-3328 - [ANCHORS] Marco Túlio Ribeiro, Sameer Singh, Carlos Guestrin: Anchors: High-Precision Model-Agnostic Explanations. AAAI 2018: pp 1527-1535
- [SURVEY] Riccardo Guidotti, Anna Monreale, Franco Turini, Dino Pedreschi, Fosca Giannotti: A Survey Of Methods For Explaining Black Box Models. ACM Computing Surveys Vol. 51, No. 5 (2018)
- [Prototype Explanations] Chaofan Chen, Oscar Li, Daniel Tao, Alina Barnett, Cynthia Rudin, Jonathan Su: This Looks Like That: Deep Learning for Interpretable Image Recognition. NeurIPS 2019: 8928-8939
Big projects on explainable/interpretable AI
- TAILOR ICT-48 project (Foundations of Trustworthy AI integrating Learning, Optimisation and Reasoning): focus on WP3 about Trustworthy AI
- DARPA XAI (until 2018)
- ERC grant Fosca GIANNOTTI, XAI : Science and technology for the explanation of AI decision making (2019-10-01 — 2024-09-30)
Miscellaneous
- A page with (good) additional resources about XAI
