Year Author Title Publication Title Url Date 

2018 Alvarez Melis, David; Jaakkola, Tommi Towards Robust Interpretability with Self-Explaining Neural Networks Advances in Neural Information Processing Systems 31 http://papers.nips.cc/paper/8003-towards-robust-interpretability-with-self-explaining- 2018 

neural-networks.pdf 

2017 Samek, Wojciech; Binder, Alexander; Evaluating the Visualization of What a Deep Neural Network Has Learned IEEE Transactions on Neural Networks and Learning Systems 2017-11 

Montavon, Grégoire; Lapuschkin, Sebastian; 

Müller, Klaus-Robert 

2018 Gilpin, Leilani H.; Bau, David; Yuan, Ben Z.; Explaining Explanations: An Overview of Interpretability of Machine Learning 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) 2018-10 

Bajwa, Ayesha; Specter, Michael; Kagal, 

Lalana 

2018 Robnik-Šikonja, Marko; Bohanec, Marko Perturbation-Based Explanations of Prediction Models Human and Machine Learning: Visible, Explainable, Trustworthy and Transparent https://doi.org/10.1007/978-3-319-90403-0_9 2018 

2007 Tintarev, Nava; Masthoff, Judith A Survey of Explanations in Recommender Systems 2007 IEEE 23rd International Conference on Data Engineering Workshop 2007-04 

2019 Miller, Tim Explanation in artificial intelligence: Insights from the social sciences Artificial Intelligence http://www.sciencedirect.com/science/article/pii/S0004370218305988 2/1/19 

2009 Chang, Jonathan; Gerrish, Sean; Wang, Reading Tea Leaves: How Humans Interpret Topic Models Advances in Neural Information Processing Systems 22 http://papers.nips.cc/paper/3700-reading-tea-leaves-how-humans-interpret-topic- 2009 

Chong; Boyd-graber, Jordan L.; Blei, David M. models.pdf 

2019 Lage, Isaac; Chen, Emily; He, Jeffrey; An Evaluation of the Human-Interpretability of Explanation Workshop on Correcting and Critiquing Trends in Machine Learning, 2019 

Narayanan, Menaka; Kim, Been; Gershman, 

Sam; Doshi-Velez, Finale 

2018 Narayanan, Menaka; Chen, Emily; He, Jeffrey; How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the arXiv:1802.00682 [cs] http://arxiv.org/abs/1802.00682 2/2/18 

Kim, Been; Gershman, Sam; Doshi-Velez, Human-Interpretability of Explanation 

Finale 

2019 Schmidt, Philipp; Biessmann, Felix Quantifying Interpretability and Trust in Machine Learning Systems Workshop on Network Interpretability for Deep Learning, 2019 

2020 Bhatt, Umang; Moura, José M. F.; Weller, Evaluating and Aggregating Feature-based Model Explanations https://www.ijcai.org/proceedings/2020/417 7/9/20 

Adrian 

2019 Hooker, Sara; Erhan, Dumitru; Kindermans, A Benchmark for Interpretability Methods in Deep Neural Networks Advances in Neural Information Processing Systems 32 http://papers.nips.cc/paper/9167-a-benchmark-for-interpretability-methods-in-deep- 2019 

Pieter-Jan; Kim, Been neural-networks.pdf 

2020 Rieger, Laura; Hansen, Lars Kai IROF: a low resource evaluation metric for explanation methods Workshop, 2020 

2018 Ancona, Marco; Ceolini, Enea; Öztireli, Towards better understanding of gradient-based attribution methods for Deep Neural Networks https://openreview.net/forum?id=Sy21R9JAW 2/15/18 

Cengiz; Gross, Markus 

2019 Yeh, Chih-Kuan; Hsieh, Cheng-Yu; Suggala, On the (In)fidelity and Sensitivity of Explanations Advances in Neural Information Processing Systems 32 http://papers.nips.cc/paper/9278-on-the-infidelity-and-sensitivity-of-explanations.pdf 2019 

Arun; Inouye, David I; Ravikumar, Pradeep K