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[1] Andrew Cropper,et al. Turning 30: New Ideas in Inductive Logic Programming , 2020, ArXiv.
[2] Jude W. Shavlik,et al. in Advances in Neural Information Processing , 1996 .
[3] Ankur Taly,et al. Axiomatic Attribution for Deep Networks , 2017, ICML.
[4] Cuntai Guan,et al. A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI , 2019, IEEE Transactions on Neural Networks and Learning Systems.
[5] Ulrike von Luxburg,et al. A tutorial on spectral clustering , 2007, Stat. Comput..
[6] Rob Fergus,et al. Visualizing and Understanding Convolutional Networks , 2013, ECCV.
[7] Alberto Maria Segre,et al. Programs for Machine Learning , 1994 .
[8] Sarah Adel Bargal,et al. NBDT: Neural-Backed Decision Trees , 2020, ArXiv.
[9] Jianlong Zhou,et al. Evaluating the Quality of Machine Learning Explanations: A Survey on Methods and Metrics , 2021, Electronics.
[10] Klaus-Robert Müller,et al. Learning how to explain neural networks: PatternNet and PatternAttribution , 2017, ICLR.
[11] Motoaki Kawanabe,et al. How to Explain Individual Classification Decisions , 2009, J. Mach. Learn. Res..
[12] Quanshi Zhang,et al. Visual interpretability for deep learning: a survey , 2018, Frontiers of Information Technology & Electronic Engineering.
[13] Stephen Muggleton,et al. Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP , 2018, Machine Learning.
[14] Xue Liu,et al. An Empirical Evaluation of Rule Extraction from Recurrent Neural Networks , 2017, Neural Computation.
[15] Natalia Díaz Rodríguez,et al. Explainability in Deep Reinforcement Learning , 2020, Knowl. Based Syst..
[16] Martin Wattenberg,et al. SmoothGrad: removing noise by adding noise , 2017, ArXiv.
[17] Mennatallah El-Assady,et al. explAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learning , 2019, IEEE Transactions on Visualization and Computer Graphics.
[18] Joseph D. Janizek,et al. Explaining Explanations: Axiomatic Feature Interactions for Deep Networks , 2020, J. Mach. Learn. Res..
[19] Quanshi Zhang,et al. Interpreting CNN knowledge via an Explanatory Graph , 2017, AAAI.
[20] Holger Hermanns,et al. What Do We Want From Explainable Artificial Intelligence (XAI)? - A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research , 2021, Artif. Intell..
[21] Alexander Binder,et al. On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation , 2015, PloS one.
[22] Naftali Tishby,et al. Opening the Black Box of Deep Neural Networks via Information , 2017, ArXiv.
[23] Satya M. Muddamsetty,et al. Introducing and assessing the explainable AI (XAI)method: SIDU , 2021 .
[24] Scott Lundberg,et al. A Unified Approach to Interpreting Model Predictions , 2017, NIPS.
[25] Stephen Muggleton,et al. How Does Predicate Invention Affect Human Comprehensibility? , 2016, ILP.
[26] Bolei Zhou,et al. Learning Deep Features for Discriminative Localization , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[27] Brandon M. Greenwell,et al. Interpretable Machine Learning , 2019, Hands-On Machine Learning with R.
[28] Eneldo Loza Mencía,et al. DeepRED - Rule Extraction from Deep Neural Networks , 2016, DS.
[29] Franco Turini,et al. A Survey of Methods for Explaining Black Box Models , 2018, ACM Comput. Surv..
[30] Dong Tian,et al. FoldingNet: Interpretable Unsupervised Learning on 3D Point Clouds , 2017, ArXiv.
[31] Amit Dhurandhar,et al. One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques , 2019, ArXiv.
[32] Lalana Kagal,et al. Explaining Explanations: An Overview of Interpretability of Machine Learning , 2018, 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA).
[33] Klaus-Robert Müller,et al. iNNvestigate neural networks! , 2018, J. Mach. Learn. Res..
[34] Trevor Darrell,et al. Grounding Visual Explanations , 2018, ECCV.
[35] Carlos Guestrin,et al. "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.
[36] Bolei Zhou,et al. Interpretable Basis Decomposition for Visual Explanation , 2018, ECCV.
[37] Kate Saenko,et al. RISE: Randomized Input Sampling for Explanation of Black-box Models , 2018, BMVC.
[38] Heng Tao Shen,et al. Principal Component Analysis , 2009, Encyclopedia of Biometrics.
[39] Jaime S. Cardoso,et al. Machine Learning Interpretability: A Survey on Methods and Metrics , 2019, Electronics.
[40] Ute Schmid,et al. Expressive Explanations of DNNs by Combining Concept Analysis with ILP , 2020, KI.
[41] Trevor Darrell,et al. Textual Explanations for Self-Driving Vehicles , 2018, ECCV.
[42] Alexander Binder,et al. Unmasking Clever Hans predictors and assessing what machines really learn , 2019, Nature Communications.
[43] Amina Adadi,et al. Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI) , 2018, IEEE Access.
[44] L. Longo,et al. Explainable Artificial Intelligence: a Systematic Review , 2020, ArXiv.
[45] William J. Clancey,et al. Principles of Explanation in Human-AI Systems , 2021, ArXiv.
[46] Amit Dhurandhar,et al. Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives , 2018, NeurIPS.
[47] Cynthia Rudin,et al. This Looks Like That: Deep Learning for Interpretable Image Recognition , 2018 .
[48] Kun Qian,et al. A Survey of the State of Explainable AI for Natural Language Processing , 2020, AACL/IJCNLP.
[49] Gereon Weiss,et al. Benchmarking Uncertainty Estimation Methods for Deep Learning With Safety-Related Metrics , 2020, SafeAI@AAAI.
[50] Klaus-Robert Müller,et al. From Clustering to Cluster Explanations via Neural Networks , 2019, IEEE transactions on neural networks and learning systems.
[51] Roberto Cipolla,et al. Concrete Problems for Autonomous Vehicle Safety: Advantages of Bayesian Deep Learning , 2017, IJCAI.
[52] Anind K. Dey,et al. Understanding and Using Context , 2001, Personal and Ubiquitous Computing.
[53] Przemyslaw Biecek,et al. The grammar of interactive explanatory model analysis , 2020, Data mining and knowledge discovery.
[54] Andrew Zisserman,et al. Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps , 2013, ICLR.
[55] Ute Schmid,et al. The Next Generation of Medical Decision Support: A Roadmap Toward Transparent Expert Companions , 2020, Frontiers in Artificial Intelligence.
[56] Guang-Zhong Yang,et al. XAI—Explainable artificial intelligence , 2019, Science Robotics.
[57] Applying Explainable Artificial Intelligence for Deep Learning Networks to Decode Facial Expressions of Pain and Emotions , 2018 .
[58] Michael Chromik,et al. A Taxonomy for Human Subject Evaluation of Black-Box Explanations in XAI , 2020, ExSS-ATEC@IUI.
[59] Martin Wattenberg,et al. Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) , 2017, ICML.
[60] Michael Siebers,et al. Explaining Black-Box Classifiers with ILP - Empowering LIME with Aleph to Approximate Non-linear Decisions with Relational Rules , 2018, ILP.
[61] Rich Caruana,et al. How Interpretable and Trustworthy are GAMs? , 2020, KDD.
[62] Quanshi Zhang,et al. Explaining Neural Networks Semantically and Quantitatively , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[63] Kate Saenko,et al. Black-box Explanation of Object Detectors via Saliency Maps , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[64] C. Rudin,et al. Concept whitening for interpretable image recognition , 2020, Nature Machine Intelligence.
[65] Andrea Vedaldi,et al. Net2Vec: Quantifying and Explaining How Concepts are Encoded by Filters in Deep Neural Networks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[66] William Eberle,et al. Explainable Artificial Intelligence Approaches: A Survey , 2021, ArXiv.
[67] Jingtao Yao. Knowledge extracted from trained neural networks: What's next? , 2005, SPIE Defense + Commercial Sensing.
[68] Rich Caruana,et al. InterpretML: A Unified Framework for Machine Learning Interpretability , 2019, ArXiv.
[69] Eric M. S. P. Veith,et al. Explainable Reinforcement Learning: A Survey , 2020, CD-MAKE.
[70] Bjorn Ommer,et al. A Disentangling Invertible Interpretation Network for Explaining Latent Representations , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[71] Percy Liang,et al. Understanding Black-box Predictions via Influence Functions , 2017, ICML.
[72] Lee Lacy,et al. Defense Advanced Research Projects Agency (DARPA) Agent Markup Language Computer Aided Knowledge Acquisition , 2005 .
[73] John F. Canny,et al. Interpretable Learning for Self-Driving Cars by Visualizing Causal Attention , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[74] Trevor Darrell,et al. Generating Visual Explanations , 2016, ECCV.
[75] Claes Lundström,et al. Survey of XAI in Digital Pathology , 2020, AI and ML for Digital Pathology.
[76] Geoffrey E. Hinton,et al. Dynamic Routing Between Capsules , 2017, NIPS.
[77] Kristian Kersting,et al. Explanatory Interactive Machine Learning , 2019, AIES.
[78] Juliana Jansen Ferreira,et al. What Are People Doing About XAI User Experience? A Survey on AI Explainability Research and Practice , 2020, HCI.
[79] Zachary Chase Lipton. The mythos of model interpretability , 2016, ACM Queue.
[80] J. Friedman. Greedy function approximation: A gradient boosting machine. , 2001 .
[81] Weng-Keen Wong,et al. Explanatory Debugging: Supporting End-User Debugging of Machine-Learned Programs , 2010, VL/HCC.
[82] Seth Flaxman,et al. European Union Regulations on Algorithmic Decision-Making and a "Right to Explanation" , 2016, AI Mag..
[83] Bernt Schiele,et al. Interpretability Beyond Classification Output: Semantic Bottleneck Networks , 2019, ArXiv.
[84] Bolei Zhou,et al. Network Dissection: Quantifying Interpretability of Deep Visual Representations , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[85] Jerry Alan Fails,et al. Interactive machine learning , 2003, IUI '03.
[86] Dietmar Jannach,et al. A systematic review and taxonomy of explanations in decision support and recommender systems , 2017, User Modeling and User-Adapted Interaction.
[87] Wei Bai,et al. Quantitative Evaluations on Saliency Methods: An Experimental Study , 2020, ArXiv.
[88] Artur S. d'Avila Garcez,et al. Logic Tensor Networks for Semantic Image Interpretation , 2017, IJCAI.
[89] Xu Chen,et al. Explainable Recommendation: A Survey and New Perspectives , 2018, Found. Trends Inf. Retr..
[90] Emil Pitkin,et al. Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation , 2013, 1309.6392.
[91] Klaus-Robert Müller,et al. Towards Explainable Artificial Intelligence , 2019, Explainable AI.
[92] Amitojdeep Singh,et al. Explainable Deep Learning Models in Medical Image Analysis , 2020, J. Imaging.
[93] Thomas Brox,et al. Striving for Simplicity: The All Convolutional Net , 2014, ICLR.
[94] T. Kathirvalavakumar,et al. Rule extraction from neural networks — A comparative study , 2012, International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012).
[95] Sebastian Thrun,et al. Extracting Rules from Artifical Neural Networks with Distributed Representations , 1994, NIPS.
[96] Deborah Silver,et al. Feature Visualization , 1994, Scientific Visualization.
[97] William J. Clancey,et al. Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI , 2019, ArXiv.
[98] Been Kim,et al. Towards A Rigorous Science of Interpretable Machine Learning , 2017, 1702.08608.
[99] Adel Rahimi,et al. The Need for Standardized Explainability , 2020, ArXiv.
[100] Marcin Detyniecki,et al. Concept Tree: High-Level Representation of Variables for More Interpretable Surrogate Decision Trees , 2019, ArXiv.
[101] Wojciech Samek,et al. Toward Interpretable Machine Learning: Transparent Deep Neural Networks and Beyond , 2020, ArXiv.
[102] Tameru Hailesilassie,et al. Rule Extraction Algorithm for Deep Neural Networks: A Review , 2016, ArXiv.
[103] Bettina Finzel,et al. Mutual Explanations for Cooperative Decision Making in Medicine , 2020, KI - Künstliche Intelligenz.
[104] Andrea Omicini,et al. On the integration of symbolic and sub-symbolic techniques for XAI: A survey , 2020, Intelligenza Artificiale.
[105] Xue Liu,et al. A Comparative Study of Rule Extraction for Recurrent Neural Networks , 2018, 1801.05420.
[106] Abhishek Das,et al. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).
[107] Andrea Vedaldi,et al. Interpretable Explanations of Black Boxes by Meaningful Perturbation , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[108] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[109] Alex Kendall,et al. What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? , 2017, NIPS.
[110] James Zou,et al. Towards Automatic Concept-based Explanations , 2019, NeurIPS.
[111] Hu Wang,et al. ReNN: Rule-embedded Neural Networks , 2018, 2018 24th International Conference on Pattern Recognition (ICPR).
[112] Marcel van Gerven,et al. Explainable Deep Learning: A Field Guide for the Uninitiated , 2020, J. Artif. Intell. Res..
[113] Sotiris Kotsiantis,et al. Explainable AI: A Review of Machine Learning Interpretability Methods , 2020, Entropy.
[114] Chun-Liang Li,et al. On Completeness-aware Concept-Based Explanations in Deep Neural Networks , 2020, NeurIPS.
[115] Francisco Herrera,et al. Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI , 2020, Inf. Fusion.
[116] Christian Biemann,et al. What do we need to build explainable AI systems for the medical domain? , 2017, ArXiv.