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@@ -4,5 +4,21 @@
 * [Reinforcement Learning for NLP and Text](Re-inforcement-Learning-for-NLP-and-Text)
 * [Tutorials and Introductory remarks](Tutorials-and-Introductory-remarks)
 
+# Resources
+
+## Tutorials
+|Title|Venue|Authors|slides|
+|-----|-----|-------|------|
+|Interpretable ML: The fuss, the concrete and the questions|ICML 2017|Kim and Doshi-Velez|[link](https://people.csail.mit.edu/beenkim/papers/BeenK_FinaleDV_ICML2017_tutorial.pdf)|
+|Tutorial on XKDD| XKDD Workshop ECML 2018|Monreale et al.|[link](https://kdd.isti.cnr.it/xkdd2019/pkdd2019xkdd_tutorial_last.pdf)|
+|Explainable AI in Industry|KDD 2019|Geyik et al., LinkedIn|[link](https://www.slideshare.net/KrishnaramKenthapadi/explainable-ai-in-industry-kdd-2019-tutorial)|
+|On Explainable AI:From Theory to Motivation, Applications and Limitations|AAAI 2019|many|[link](https://xaitutorial2019.github.io/#)|
+
+## Overviews, Surveys, Reviews
+|Title|Author|link|
+|-----|------|----|
+|The Mythos of Model Interpretability|Lipton|[arxiv](https://arxiv.org/abs/1606.03490)|
+|Towards a Rigorous Science of Interpretable Machine Learning|Doshi-Velez and Kim|[arxiv](https://arxiv.org/abs/1702.08608)|
+|A Survey of Methods for Explaining Black Box Models| Guidotti et al.|[arxiv](https://arxiv.org/abs/1802.01933)|