Contents
- Limitations of Interpretability
- NeurIPS 2019 Interpretability Roundup
- Interpretability by Design
- Reinforcement Learning for NLP and Text
- Tutorials and Introductory remarks
- Concept-based Explanations
Resources
Tutorials
Title | Venue | Authors | slides |
---|---|---|---|
Interpretable ML: The fuss, the concrete and the questions | ICML 2017 | Kim and Doshi-Velez | link |
Tutorial on XKDD | XKDD Workshop ECML 2018 | Monreale et al. | link |
Explainable AI in Industry | KDD 2019 | Geyik et al., LinkedIn | link |
On Explainable AI:From Theory to Motivation, Applications and Limitations | AAAI 2019 | many | link |
Overviews, Surveys, Reviews
Title | Author | link |
---|---|---|
The Mythos of Model Interpretability | Lipton | arxiv |
Towards a Rigorous Science of Interpretable Machine Learning | Doshi-Velez and Kim | arxiv |
A Survey of Methods for Explaining Black Box Models | Guidotti et al. | arxiv |
Interpretability of Machine Learning Models and Representations: an Introduction | Adrien Bibal and Benoît Frénay |
Books
Title | Author | link |
---|---|---|
Interpretable ML: A Guide for Making Black Box Models Explainable. | Molnar | link |
Limitations of Interpretable Machine Learning Methods | LMU seminar | link |
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