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Explainable Artificial Intelligence (XAI) is an emerging area of research in the field of Artificial Intelligence (AI).
1906
Earlier work this paper cites.
1907
Earlier work this paper cites.
Lloyd S Shapley. “A value for n-person games”. In:Contributions to the Theory of Games2.28 (1953), pp. 307–317
1953
Earlier work this paper cites.
REITER, E., & DALE, R. (1997). Building applied natural language generation systems. Natural Language Engineering, 3(1), 57-87. doi:10.1017/S1351324997001502
1997
Earlier work this paper cites.
Y. X. Zhong, ”A Cognitive Approach to Artificial Intelligence Research,” 2006 5th IEEE International Conference on Cognitive Informatics, Beijing, China, 2006, pp. 90-100, doi: 10.1109/COGINF.2006.365682
2006
Earlier work this paper cites.
2006
Earlier work this paper cites.
S. J. du Preez, M. Lall and S. Sinha, ”An intelligent web-based voice chat bot,” IEEE EUROCON 2009, 2009, pp. 386-391, doi: 10.1109/EURCON.2009.5167660
2009
Earlier work this paper cites.
2009
Earlier work this paper cites.
J. Wang, J. Yang, K. Yu, F. Lv, T. Huang and Y. Gong, ”Locality-constrained Linear Coding for image classification,” 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, 2010, pp. 3360-3367, doi: 10.1109/CVPR.2010.5540018
2010
Earlier work this paper cites.
Glorot, X., Bordes, A., & Bengio, Y. (2011). Domain adaptation for large-scale sentiment classification: A deep learning approach. In Proceedings of the 28th international conference on international conference on machine learning(513–520)
2011
Earlier work this paper cites.
Vijay N. Garla, Cynthia Brandt, Ontology-guided feature engineering for clinical text classification, Journal of Biomedical Informatics, Volume 45, Issue 5,2012, Pages 992-998, ISSN 1532-0464, https://doi.org/10.1016/j.jbi.2012.04.010
2012
Earlier work this paper cites.
Chen, Y., Zhou, Y., Zhu, S., & Xu, H. (2012). Detecting offensive language in social media to protect adolescent online safety. In2012 international conference on privacy, security, risk and trust and 2012 international conference on social computing
2012
Earlier work this paper cites.
2013
Earlier work this paper cites.
Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek. On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PloS one, 10(7):e0130140, 2015
2015
Earlier work this paper cites.
Nikos Voskarides, Edgar Meij, Manos Tsagkias,Maarten de Rijke, and Wouter Weerkamp. 2015.Learning to explain entity relationships in knowledge graphs. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conferenceon Natural Language Processing
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
Scott M Lundberg and Su-In Lee. A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems, pages 4765–4774, 2017
2017
Earlier work this paper cites.
Igami. (2017). “Artificial intelligence as structural estimation: Eco-nomic interpretations of deep blue, bonanza, and AlphaGo.”
2017
Earlier work this paper cites.
Qizhe Xie, Xuezhe Ma, Zihang Dai, and Eduard Hovy.2017. An interpretable knowledge transfer model for knowledge base completion.In Proceedings of the 55 th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers),pages 950–962, Vancouver, Canada. Association for Computational Linguistics
2017
Earlier work this paper cites.
Ashish Vaswani, Noam Shazeer, Niki Parmar, JakobUszkoreit, Llion Jones, Aidan N. Gomez, ŁukaszKaiser, and Illia Polosukhin. 2017. Attention is all you need. In NeuralIPS
2017
Earlier work this paper cites.
Abdalghani Abujabal, Mohamed Yahya, Mirek Riedewald, and Gerhard Weikum. 2017. Automated Template Generation for Question Answering over Knowledge Graphs. In Proceedings of the 26th International Conference on World Wide Web (WWW ’17). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 1191–1200. DOI:https://doi.org/10.1145/3038912.3052583
2017
Earlier work this paper cites.
2017
Cited alongside, same era.
Yan, C., LindK.R. Explaining nonlinear classification decisions with deep taylor decomposition. Pattern Recognit. 2017, 65, 211–222
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Dixon, L., Li, J., Sorensen, J., Thain, N., & Vasserman, L. (2018). Measuring and mitigating unintended bias in text classification. In Proceedings of the 2018 aaai/acm conference on ai, ethics, and society(pp. 67–73)
2018
Later among the works it cites.
Pereira, S.; Meier, R.; Alves, V.; Reyes, M.; Silva, C.A. Automatic brain tumor grading from MRI data using convolutional neural networks and quality assessment. In Understanding and Interpreting Machine Learning in Medical Image Computing Applications; Springer: Cham, Switzerland, 2018; pp. 106–114
2018
Later among the works it cites.
James Thorne,Andreas Vlachos,Christos Christodoulopoulos, and Arpit Mittal. 2019. Gener-ating token-level explanations for natural language inference. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Minneapolis, Minnesota. Association for Computational Linguistics
2019
Later among the works it cites.
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2017
Cited alongside, same era.
Baziotis, C., Pelekis, N., & Doulkeridis, C. (2017). Datastories at semeval-2017 task 4:Deep lstm with attention for message-level and topic-based sentiment analysis. In Proceedings of the 11th international workshop on semantic evaluation. (semeval-2017)(pp. 747–754)
2017
Cited alongside, same era.
Chatzakou, D., Kourtellis, N., Blackburn, J., De Cristofaro, E., Stringhini, G., & Vakali,A. (2017). Mean birds: Detecting aggression and bullying on twitter. In Proceedings of the 2017 acm on web science conference(pp. 13–22)
2017
Cited alongside, same era.
Davidson, T., Warmsley, D., Macy, M., & Weber, I. (2017). Automated hate speech detection and the problem of offensive language. In Eleventh international aaai conference on web and social media
2017
Cited alongside, same era.
Online harassment 2017. Pew Research Center
2017
Cited alongside, same era.
A. Adadi and M. Berrada, ”Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI),” in IEEE Access, vol. 6, pp. 52138-52160, 2018, doi: 10.1109/ACCESS.2018.2870052
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Amit Dhurandhar, Pin-Yu Chen, Ronny Luss, Chun-Chen Tu, Paishun Ting, Karthikeyan Shanmugam, and Payel Das. Explanations based on the missing: Towards contrastive explanations with pertinent negatives. In Advances in Neural Information Processing Systems, pages 592–603, 2018
2018
Cited alongside, same era.
Robert Schwarzenberg, David Harbecke, Vivien Mack-etanz, Eleftherios Avramidis, and Sebastian Moller.2019. Train, sort, explain: Learning to diagnose translation models. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)
2019
Later among the works it cites.
Sofia Serrano and Noah A. Smith. 2019. Is attention interpretable ? In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy. Association for Computational Linguistics
2019
Later among the works it cites.
Nicolas Prollochs, Stefan Feuerriegel, and Dirk Neumann. 2019. Learning interpretable negation rules via weak supervision at document level: A reinforcement learning approach. In Proceedings of the 2019Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Minneapolis, Minnesota.Association for Computational Linguistics
2019
Later among the works it cites.
Piyawat Lertvittayakumjorn and Francesca Toni. 2019.Human-grounded evaluations of explanation methods for text classification. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Hong Kong,China. Association for Computational Linguistics
2019
Later among the works it cites.
Qiuchi Li, Benyou Wang, and Massimo Melucci. 2019.CNM: An interpretable complex valued network for matching. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers),pages 4139–4148, Minneapolis, Minnesota. Association for Computational Linguistics
2019
Later among the works it cites.
Sebastian Lapuschkin, Stephan Waldchen, Alexander Binder, Gregoir eMontavon, Wojciech Samek, and Klaus-Robert Muller. Unmasking clever hans predictors and assessing what machines really learn.Nature Communications, 10(1):1096, 2019
2019
Later among the works it cites.
A. Fernandez, F. Herrera, O. Cordon, M. Jose del Jesus, and F. Marcel-loni. Evolutionary fuzzy systems for explainable artificial intelligence:Why, when, what for, and where to?IEEE Computational Intelligence Magazine, 14(1):69–81, Feb 2019
2019
Later among the works it cites.
Founta, A. M., Chatzakou, D., Kourtellis, N., Blackburn, J., Vakali, A., & Leontiadis, I.(2019). A unified deep learning architecture for abuse detection. In Proceedings of the 10 th acm conference on web science(pp. 105–114)
2019
Later among the works it cites.
Mathew, B., Dutt, R., Goyal, P., & Mukherjee, A. (2019). Spread of hate speech in onlinesocial media. InProceedings of the 10th acm conference on web science(pp. 173–182)
2019
Later among the works it cites.
Eitel, F.; Ritter, K.; Alzheimer’s Disease Neuro imaging Initiative (ADNI). Testing the Robustness of Attribution Methods for Convolutional Neural Networks in MRI-Based Alzheimer’s Disease Classification. In Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support, ML-CDS 2019, IMIMIC 2019; Lecture Notes in Computer Science; Suzuki, K., et al., Eds.; Springer: Cham, Switzerland, 2019; Volume 11797
2019
Later among the works it cites.
Robbins, Mark D.. (2019). AI Explainability Regulations and Responsibilities
2019
Later among the works it cites.
Alejandro Barredo Arrieta et al.,Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI, DOI: 10.1016/j.inffus.2019.12.012
2019
Later among the works it cites.
2020
Later among the works it cites.
Vaishak Belle Ioannis Papantonis.,Principles and Practice of Explainable Machine Learning*, Sep 2020
2020
Later among the works it cites.
Liam Hiley, Alun Preece, Yulia Hicks, Supriyo Chakraborty, Prudhvi Gurram, and Richard Tomsett. Explaining motion relevance for activity recognition in video deep learning models, 2020
2020
Later among the works it cites.
Hrnjica, Bahrudin & Softic, Selver. (2020). Explainable AI in Manufacturing: A Predictive Maintenance Case Study
2020
Later among the works it cites.
Shukla, Bibhudhendu & Fan, Ip-Shing & Jennions, I.K.. (2020). Opportunities for Explainable Artificial Intelligence in Aerospace Predictive Maintenance
2020
Later among the works it cites.
S. Matzka, ”Explainable Artificial Intelligence for Predictive Maintenance Applications,” 2020, (AI4I), doi: 10.1109/AI4I49448.2020.00023
2020
Later among the works it cites.