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@ -16,12 +16,15 @@ This is an on-going attempt to consolidate interesting efforts in the area of un
# Surveys
* Methods for Interpreting and Understanding Deep Neural Networks. _Montavon et al. 2017_ [pdf](https://arxiv.org/pdf/1706.07979.pdf)
* The Mythos of Model Interpretability. _Lipton 2016_ [pdf](https://arxiv.org/abs/1606.03490)
* Towards A Rigorous Science of Interpretable Machine Learning _Doshi-Velez & Kim. 2017_ [pdf](https://arxiv.org/pdf/1702.08608.pdf)
* Towards A Rigorous Science of Interpretable Machine Learning. _Doshi-Velez & Kim. 2017_ [pdf](https://arxiv.org/pdf/1702.08608.pdf)
* Visualizations of Deep Neural Networks in Computer Vision: A Survey. _Seifert et al. 2017_ [pdf](https://link.springer.com/chapter/10.1007/978-3-319-54024-5_6)
* How convolutional neural network see the world - A survey of convolutional neural network visualization methods. _Qin et al. 2018_ [pdf](https://arxiv.org/abs/1804.11191)
* A brief survey of visualization methods for deep learning models from the perspective of Explainable AI. _Chalkiadakis 2018_ [pdf](https://www.macs.hw.ac.uk/~ic14/IoannisChalkiadakis_RRR.pdf)
* A Survey Of Methods For Explaining Black Box Models. _Guidotti et al. 2018_ [pdf](https://arxiv.org/pdf/1802.01933.pdf)
# Books
* A Guide for Making Black Box Models Explainable. _Molnar 2019_ [pdf](https://christophm.github.io/interpretable-ml-book/)
# A. Explaining inner-workings
## A1. Visualizing Preferred Stimuli
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* NLIZE: A Perturbation-Driven Visual Interrogation Tool for Analyzing and Interpreting Natural Language Inference Models. _Liu et al. 2018_ [pdf](http://www.sci.utah.edu/~shusenl/publications/paper_entailVis.pdf)
# B. Explaining decisions
# B. Decision explanations
## B1. Heatmaps / Attribution
#### White-box
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* Unsupervised Learning of Neural Networks to Explain Neural Networks [pdf](https://arxiv.org/abs/1805.07468)
* Automated Rationale Generation: A Technique for Explainable AI and its Effects on Human Perceptions [pdf](https://arxiv.org/abs/1901.03729)
* Rationalization: A Neural Machine Translation Approach to Generating Natural Language Explanations [pdf](https://arxiv.org/pdf/1702.07826.pdf)
* Towards robust interpretability with self-explaining neural networks. _Alvarez-Melis and Jaakola 2018._ [pdf](http://people.csail.mit.edu/tommi/papers/SENN_paper.pdf)
* Towards robust interpretability with self-explaining neural networks. _Alvarez-Melis and Jaakola 2018_ [pdf](http://people.csail.mit.edu/tommi/papers/SENN_paper.pdf)
## C. Counterfactual explanations (what would have happen)
* Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections. _Zhang et al. 2018_ [pdf](http://papers.nips.cc/paper/7736-interpreting-neural-network-judgments-via-minimal-stable-and-symbolic-corrections.pdf)
# C. Unclassified
# D. Unclassified
* Yang, S. C. H., & Shafto, P. Explainable Artificial Intelligence via Bayesian Teaching. NIPS 2017 [pdf](http://shaftolab.com/assets/papers/yangShafto_NIPS_2017_machine_teaching.pdf)
* Explainable AI for Designers: A Human-Centered Perspective on Mixed-Initiative Co-Creation [pdf](http://www.antoniosliapis.com/papers/explainable_ai_for_designers.pdf)
* ICADx: Interpretable computer aided diagnosis of breast masses. _Kim et al. 2018_ [pdf](https://arxiv.org/abs/1805.08960)