{"type":"ScholarlyArticle","title":"Benchmarking eXplainable AI - A Survey on Available Toolkits and Open Challenges","authors":[{"name":"Phuong Quynh Le","slug":"phuong-quynh-le","given":"Phuong Quynh","family":"Le","affiliation":"University of Duisburg-Essen , Germany"},{"name":"Meike Nauta","slug":"meike-nauta","given":"Meike","family":"Nauta","affiliation":"University of Duisburg-Essen , Germany; University of Twente , the Netherlands"},{"name":"Van Bach Nguyen","slug":"van-bach-nguyen","given":"Van Bach","family":"Nguyen","affiliation":"University of Duisburg-Essen , Germany"},{"name":"Shreyasi Pathak","slug":"shreyasi-pathak","given":"Shreyasi","family":"Pathak","affiliation":"University of Duisburg-Essen , Germany; University of Twente , the Netherlands"},{"name":"Jörg Schlötterer","slug":"jorg-schlotterer","given":"Jörg","family":"Schlötterer","affiliation":"University of Duisburg-Essen , Germany; University of Marburg , Germany; University of Mannheim , Germany"},{"name":"Christin Seifert","slug":"christin-seifert","given":"Christin","family":"Seifert","affiliation":"University of Duisburg-Essen , Germany; University of Marburg , Germany"}],"abstract":"The goal of Explainable AI (XAI) is to make the reasoning of a machine learning model accessible to humans, such that users of an AI system can evaluate and judge the underlying model. Due to the blackbox nature of XAI methods it is, however, hard to disentangle the contribution of a model and the explanation method to the final output. It might be unclear on whether an unexpected output is caused by the model or the explanation method. Explanation models, therefore, need to be evaluated in technical (e.g. fidelity to the model) and user-facing (correspondence to domain knowledge) terms. A recent survey has identified 29 different automated approaches to quantitatively evaluate explanations. In this work, we take an additional perspective and analyse which toolkits and data sets are available. We investigate which evaluation metrics are implemented in the toolkits and whether they produce the same results. We find that only a few aspects of explanation quality are currently covered, data sets are rare and evaluation results are not comparable across  different toolkits. Our survey can serve as a guide for the XAI community for identifying future directions of research, and most notably, standardisation of evaluation.","doi":"10.24963/ijcai.2023/747","url":"https://ijcai.org/proceedings/2023/747","pdf":"https://ijcai.org/proceedings/2023/0747.pdf","markdown":"https://ijcai.org/proceedings/2023/747.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/747","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Survey Track","track":"Survey Track","number":747,"pages":{"first":6665,"last":6673},"keywords":["Survey: Humans and AI","Survey: AI Ethics, Trust, Fairness","Survey: Machine Learning"],"videos":[],"references":[{"position":1,"raw":"[Adadi and Berrada, 2018] Amina Adadi and Mohammed Berrada. Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI). IEEE Access, 6:52138-52160, 2018.","title":"Amina Adadi and Mohammed Berrada. Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)","authors":"Berrada Adadi","year":"2018","venue":"IEEE Access","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Agarwal et al., 2021] Chirag Agarwal, Himabindu Lakkaraju, and Marinka Zitnik. Towards a unified framework for fair and stable graph representation learning. In Uncertainty in Artificial Intelligence, pages 2114-2124. PMLR, 2021.","title":"Towards a unified framework for fair and stable graph representation learning","authors":"Chirag Agarwal, Himabindu Lakkaraju, Marinka Zitnik","year":"2021","venue":"Uncertainty in Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Agarwal et al., 2022a] Chirag Agarwal, Satyapriya Kr- ishna, Eshika Saxena, Martin Pawelczyk, Nari Johnson, Isha Puri, Marinka Zitnik, and Himabindu Lakkaraju. Openxai: Towards a transparent evaluation of model ex- planations. In Thirty-sixth Conference on Neural Informa- tion Processing Systems Datasets and Benchmarks Track, 2022.","title":"Openxai: Towards a transparent evaluation of model explanations","authors":"Agarwal","year":"2022","venue":"Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Agarwal et al., 2022b] Chirag Agarwal, Owen Queen, Himabindu Lakkaraju, and Marinka Zitnik. Eval- uating explainability for graph neural networks. https://arxiv.org/abs/2208.09339, 2022.","title":"Evaluating explainability for graph neural networks","authors":"Agarwal","year":"2022","venue":null,"doi":null,"url":"https://arxiv.org/abs/2208.09339,2022","internal_url":null},{"position":5,"raw":"[Arya et al., 2020] Vijay Arya, Rachel K. E. Bellamy, Pin- Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Q. Vera Liao, Ronny Luss, Aleksandra Mojsilovi, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, and Yunfeng Zhang. Ai explain- ability 360: An extensible toolkit for understanding data and machine learning models. Journal of Machine Learn- ing Research, 21(130):1-6, 2020.","title":"Ai explainability 360: An extensible toolkit for understanding data and machine learning models","authors":"Vijay Arya, K E Rachel, Pin-Yu Bellamy, Amit Chen, Michael Dhurandhar, Hind, C Samuel, Stephanie Hoffman, Q Houde, Vera, Ronny Liao, Aleksandra Luss, Sami Mojsilovi, Pablo Mourad, Ramya Pedemonte, John T Raghavendra, Prasanna Richards, Karthikeyan Sattigeri, Moninder Shanmugam, Kush R Singh, Dennis Varshney, Yunfeng Wei, Zhang","year":"2020","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Baehrens et al., 2010] David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, and Klaus-Robert Müller. How to explain individual classifi- cation decisions. The Journal of Machine Learning Re- search, 11:1803-1831, 2010.","title":"How to explain individual classification decisions","authors":"David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, Klaus-Robert Müller","year":"2010","venue":"The Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Barredo Arrieta et al., 2020] Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador Garcia, Sergio Gil-Lopez, Daniel Molina, Richard Benjamins, Raja Chatila, and Francisco Herrera. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, June 2020.","title":"Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI","authors":"Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador Garcia, Sergio Gil-Lopez, Daniel Molina, Richard Benjamins, Raja Chatila, Francisco Herrera","year":"2020","venue":"Information Fusion","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Belaid et al., 2022] Mohamed Karim Belaid, Eyke Hüllermeier, Maximilian Rabus, and Ralf Krestel. Do We Need Another Explainable AI Method? To- ward Unifying Post-hoc XAI Evaluation Methods into an Interactive and Multi-dimensional Benchmark. https://arxiv.org/abs/2207.14160, 2022.","title":"Do We Need Another Explainable AI Method? Toward Unifying Post-hoc XAI Evaluation Methods into an Interactive and Multi-dimensional Benchmark","authors":"Mohamed Karim Belaid, Eyke Hüllermeier, Maximilian Rabus, Ralf Krestel","year":"2022","venue":null,"doi":null,"url":"https://arxiv.org/abs/2207.14160","internal_url":null},{"position":9,"raw":"[Carmichael and Scheirer, 2021] Zachariah Carmichael and Walter J. Scheirer. A framework for evaluating post hoc feature-additive explainers. https://arxiv.org/abs/2106. 08376, 2021.","title":"A framework for evaluating post hoc feature-additive explainers","authors":"Zachariah Carmichael, Walter J Scheirer","year":"2021","venue":null,"doi":null,"url":"https://arxiv.org/abs/2106.08376","internal_url":null},{"position":10,"raw":"[Chattopadhyay et al., 2019] Aditya Chattopadhyay, Anir- ban Sarkar, Prantik Howlader, and Vineeth N. Balasubra- manian. Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks. In IEEE Winter Conf. on Applications of Computer Vision, 2019.","title":"Balasubramanian. Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks","authors":"Aditya Chattopadhyay, Anirban Sarkar, Prantik Howlader, N Vineeth","year":"2019","venue":"IEEE Winter Conf. on Applications of Computer Vision","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Cui et al., 2022] Yiming Cui, Ting Liu, Wanxiang Che, Zhi- gang Chen, and Shijin Wang. ExpMRC: explainability evaluation for machine reading comprehension. Heliyon, 8(4), Apr 2022.","title":"ExpMRC: explainability evaluation for machine reading comprehension","authors":"Yiming Cui, Ting Liu, Wanxiang Che, Zhigang Chen, Shijin Wang","year":"2022","venue":"Heliyon","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[DARPA, 2016] DARPA. Explainable artificial intelligence (xai) programm. Technical report, 2016.","title":"DARPA. Explainable artificial intelligence (xai) programm","authors":null,"year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Doshi-Velez and Kim, 2018] Finale Doshi-Velez and Been Kim. Considerations for Evaluation and Generalization in Interpretable Machine Learning, pages 3-17. Springer International Publishing, Cham, 2018.","title":"Finale Doshi-Velez and Been Kim","authors":"Kim Doshi-Velez","year":"2018","venue":"Considerations for Evaluation and Generalization in Interpretable Machine Learning","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Dua and Graff, 2017] Dheeru Dua and Casey Graff. Uci machine learning repository. http://archive.ics.uci.edu/ml/ index.php, 2017. Accessed: 2022-12-01.","title":"Dheeru Dua and Casey Graff. Uci machine learning repository","authors":"Graff Dua","year":"2017","venue":null,"doi":null,"url":"http://archive.ics.uci.edu/ml/index.php,2017","internal_url":null},{"position":15,"raw":"[Freshcorn, 2022] Bryce Freshcorn. Give me some credit :: 2011 competition data -kaggle. https://www.kaggle. com/datasets/brycecf/give-me-some-credit-dataset, 2022. Accessed: 2022-12-01.","title":"Give me some credit :: 2011 competition data -kaggle","authors":"Bryce Freshcorn","year":"2022","venue":null,"doi":null,"url":"https://www.kaggle.com/datasets/brycecf/give-me-some-credit-dataset,2022","internal_url":null},{"position":16,"raw":"[Gilpin et al., 2018] Leilani H. Gilpin, David Bau, Ben Z. Yuan, Ayesha Bajwa, Michael Specter, and Lalana Kagal. Explaining explanations: An overview of interpretability of machine learning. In DSAA, pages 80-89. IEEE, 2018.","title":"Explaining explanations: An overview of interpretability of machine learning","authors":"Leilani H Gilpin, David Bau, Ben Z Yuan, Ayesha Bajwa, Michael Specter, Lalana Kagal","year":"2018","venue":"DSAA","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Graziani et al., 2021] Mara Graziani, Thomas Lompech, Henning Müller, and Vincent Andrearczyk. Evaluation and comparison of cnn visual explanations for histopathol- ogy. In Proceedings of the AAAI Conference on Artificial Intelligence Workshops (XAI-AAAI-21), February 2021.","title":"Evaluation and comparison of cnn visual explanations for histopathology","authors":"Graziani","year":"2021","venue":"Proceedings of the AAAI Conference on Artificial Intelligence Workshops (XAI-AAAI-21)","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Guidotti et al., 2018] Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi. A Survey of Methods for Explaining Black Box Models. ACM Computing Surveys, 51(5):93:1- 93:42, August 2018.","title":"A Survey of Methods for Explaining Black Box Models","authors":"Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, Dino Pedreschi","year":"2018","venue":"ACM Computing Surveys","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Hameed et al., 2022] Isha Hameed, Samuel Sharpe, Daniel Barcklow, Justin Au-Yeung, Sahil Verma, Jocelyn Huang, Brian Barr, and C. Bayan Bruss. BASED-XAI: Breaking Ablation Studies Down for Explainable Artificial Intelli- gence. In KDD Workshop on Machine Learning for Fi- nance, 2022.","title":"BASED-XAI: Breaking Ablation Studies Down for Explainable Artificial Intelligence","authors":"Isha Hameed, Samuel Sharpe, Daniel Barcklow, Justin Au-Yeung, Sahil Verma, Jocelyn Huang, Brian Barr, C Bayan Bruss","year":"2022","venue":"KDD Workshop on Machine Learning for Finance","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Hedström et al., 2022] Anna Hedström, Leander Weber, Dilyara Bareeva, Franz Motzkus, Wojciech Samek, Se- bastian Lapuschkin, and Marina M. C. Höhne. Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations. https://arxiv.org/abs/2202. 06861, 2022.","title":"Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations","authors":"Anna Hedström, Leander Weber, Dilyara Bareeva, Franz Motzkus, Wojciech Samek, Sebastian Lapuschkin, Marina M C Höhne","year":"2022","venue":null,"doi":null,"url":"https://arxiv.org/abs/2202.06861","internal_url":null},{"position":21,"raw":"[Holter et al., 2018] Steffen Holter, Oscar Gomez, and Enrico Bertini. Fico explainable machine learning challenge. https://community.fico.com/s/ explainable-machine-learning-challenge, 2018. Ac- cessed: 2022-12-01.","title":"Fico explainable machine learning challenge","authors":"Steffen Holter, Oscar Gomez, Enrico Bertini","year":null,"venue":null,"doi":null,"url":"2022-12-01","internal_url":null},{"position":22,"raw":"[Hoofnagle et al., 2019] Chris Jay Hoofnagle, Bart van der Sloot, and Frederik Zuiderveen Borgesius. The european union general data protection regulation: what it is and what it means. Information & Communications Technol- ogy Law, 28(1):65-98, 2019.","title":"The european union general data protection regulation: what it is and what it means","authors":"Chris Jay Hoofnagle, Bart Van Der Sloot, Frederik Zuiderveen Borgesius","year":"2019","venue":"Information & Communications Technology Law","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Huang et al., 2022] Wei Huang, Xingyu Zhao, Gaojie Jin, and Xiaowei Huang. Safari: Versatile and efficient eval- uations for robustness of interpretability. https://arxiv.org/ abs/2208.09418, 2022.","title":"Safari: Versatile and efficient evaluations for robustness of interpretability","authors":"Wei Huang, Xingyu Zhao, Gaojie Jin, Xiaowei Huang","year":"2022","venue":null,"doi":null,"url":"https://arxiv.org/abs/2208.09418","internal_url":null},{"position":24,"raw":"[Karunagaran et al., 2022] Surya Karunagaran, Ana Lucic, and Christine Custis. Xai toolsheet: Towards a documenta- tion framework for xai tools. In Workshop on Explainable Artificial Intelligence (XAI), IJCAI, 2022.","title":"Xai toolsheet: Towards a documentation framework for xai tools","authors":"Karunagaran","year":"2022","venue":"Workshop on Explainable Artificial Intelligence (XAI), IJCAI","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Kokhlikyan et al., 2020] Narine Kokhlikyan, Vivek Miglani, Miguel Martin, Edward Wang, Bilal Al- sallakh, Jonathan Reynolds, Alexander Melnikov, Natalia Kliushkina, Carlos Araya, Siqi Yan, and Orion Reblitz-Richardson. Captum: A unified and generic model interpretability library for PyTorch. https://arxiv.org/abs/2009.07896, 2020.","title":"Captum: A unified and generic model interpretability library for PyTorch","authors":"Narine Kokhlikyan, Vivek Miglani, Miguel Martin, Edward Wang, Bilal Alsallakh, Jonathan Reynolds, Alexander Melnikov, Natalia Kliushkina, Carlos Araya, Siqi Yan, Orion Reblitz-Richardson","year":"2020","venue":null,"doi":null,"url":"https://arxiv.org/abs/2009.07896","internal_url":null},{"position":26,"raw":"[Li et al., 2022] Xuhong Li, Haoyi Xiong, Xingjian Li, Xu- anyu Wu, Zeyu Chen, and Dejing Dou. Interpretdl: Ex- plaining deep models in paddlepaddle. Journal of Machine Learning Research, 23(197):1-6, 2022.","title":"Interpretdl: Explaining deep models in paddlepaddle","authors":"Li","year":"2022","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Lin et al., 2021] Yi-Shan Lin, Wen-Chuan Lee, and Z. Berkay Celik. What Do You See? Evaluation of Explainable Artificial Intelligence (XAI) Interpretability through Neural Backdoors. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, KDD '21, page 1027-1035, New York, NY, USA, 2021. Association for Computing Machinery.","title":"What Do You See? Evaluation of Explainable Artificial Intelligence (XAI) Interpretability through Neural Backdoors","authors":"Yi-Shan Lin, Wen-Chuan Lee, Z Berkay Celik","year":"2021","venue":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, KDD '21","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Liu et al., 2021] Yang Liu, Sujay Khandagale, Sujay Khandagale, Colin White, and Willie Neiswanger. Syn- thetic Benchmarks for Scientific Research in Explainable Machine Learning. In J. Vanschoren and S. Yeung, editors, Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks, volume 1, 2021.","title":"Synthetic Benchmarks for Scientific Research in Explainable Machine Learning","authors":"Yang Liu, Sujay Khandagale, Sujay Khandagale, Colin White, Willie Neiswanger","year":"2021","venue":"Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Lopes et al., 2022] Pedro Lopes, Eduardo Silva, Cristiana Braga, Tiago Oliveira, and Luís Rosado. Xai systems eval- uation: A review of human and computer-centred meth- ods. Applied Sciences, 12(19), 2022.","title":"Xai systems evaluation: A review of human and computer-centred methods","authors":"Pedro Lopes, Eduardo Silva, Cristiana Braga, Tiago Oliveira, Luís Rosado","year":"2022","venue":"Applied Sciences","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Lundberg and Lee, 2017] Scott M Lundberg and Su-In Lee. A unified approach to interpreting model predictions. In I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fer- gus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 30. Cur- ran Associates, Inc., 2017.","title":"A unified approach to interpreting model predictions","authors":"M Scott, Su-In Lundberg, Lee","year":"2017","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Mitchell et al., 2019] Margaret Mitchell, Simone Wu, An- drew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. Model cards for model reporting. In Pro- ceedings of the Conference on Fairness, Accountability, and Transparency, FAT* '19, page 220-229, New York, NY, USA, 2019. Association for Computing Machinery.","title":"Model cards for model reporting","authors":"Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa, Deborah Raji, Timnit Gebru","year":"2019","venue":"Proceedings of the Conference on Fairness, Accountability, and Transparency, FAT* '19","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Mohseni et al., 2021] Sina Mohseni, Niloofar Zarei, and Eric D. Ragan. A multidisciplinary survey and framework for design and evaluation of explainable ai systems. ACM Trans. Interact. Intell. Syst., 11(3-4), sep 2021.","title":"A multidisciplinary survey and framework for design and evaluation of explainable ai systems","authors":"Sina Mohseni, Niloofar Zarei, Eric D Ragan","year":"2021","venue":"ACM Trans. Interact. Intell. Syst","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Nauta et al., 2023] Meike Nauta, Jan Trienes, Shreyasi Pathak, Elisa Nguyen, Michelle Peters, Yasmin Schmitt, Jörg Schlötterer, Maurice van Keulen, and Christin Seifert. From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explain- able AI. ACM Comput. Surv., 2023.","title":"From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI","authors":"Meike Nauta, Jan Trienes, Shreyasi Pathak, Elisa Nguyen, Michelle Peters, Yasmin Schmitt, Jörg Schlötterer, Maurice Van Keulen, Christin Seifert","year":"2023","venue":"ACM Comput. Surv","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Ngai and Rudzicz, 2022] Hillary Ngai and Frank Rudzicz. Doctor xavier: Explainable diagnosis on physician-patient dialogues and xai evaluation. https://arxiv.org/abs/2204. 10178, 2022.","title":"Doctor xavier: Explainable diagnosis on physician-patient dialogues and xai evaluation","authors":"Rudzicz Ngai, Hillary Ngai, Frank Rudzicz","year":"2022","venue":null,"doi":null,"url":"https://arxiv.org/abs/2204.10178","internal_url":null},{"position":35,"raw":"[Ribeiro et al., 2016] Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. \"Why Should I Trust You?\": Ex- plaining the Predictions of Any Classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '16, page 1135-1144, New York, NY, USA, 2016. Association for Computing Machinery.","title":"Why Should I Trust You?\": Explaining the Predictions of Any Classifier","authors":"Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin","year":"2016","venue":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '16","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Selvaraju et al., 2017] Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. Grad-cam: Visual explanations from deep networks via gradient-based localization. In 2017 IEEE International Conference on Computer Vision (ICCV), pages 618-626, 2017.","title":"Grad-cam: Visual explanations from deep networks via gradient-based localization","authors":"R Ramprasaath, Michael Selvaraju, Abhishek Cogswell, Ramakrishna Das, Devi Vedantam, Dhruv Parikh, Batra","year":"2017","venue":"IEEE International Conference on Computer Vision (ICCV)","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Sundararajan et al., 2017] Mukund Sundararajan, Ankur Taly, and Qiqi Yan. Axiomatic attribution for deep net- works. In Proceedings of the 34th International Con- ference on Machine Learning -Volume 70, ICML'17, pages 3319-3328, Sydney, NSW, Australia, August 2017. JMLR.org.","title":"Axiomatic attribution for deep networks","authors":"Mukund Sundararajan, Ankur Taly, Qiqi Yan","year":"2017","venue":"Proceedings of the 34th International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Vilone and Longo, 2021] Giulia Vilone and Luca Longo. Notions of explainability and evaluation approaches for explainable artificial intelligence. Information Fusion, 76:89-106, 2021.","title":"Giulia Vilone and Luca Longo. Notions of explainability and evaluation approaches for explainable artificial intelligence","authors":"Longo Vilone","year":"2021","venue":"Information Fusion","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Yang and Kim, 2019] Mengjiao Yang and Been Kim. Benchmarking Attribution Methods with Relative Feature Importance. https://arxiv.org/abs/1907.09701, 2019.","title":"Benchmarking Attribution Methods with Relative Feature Importance","authors":"Yang, Kim ;, Mengjiao Yang, Been Kim","year":"2019","venue":null,"doi":null,"url":"https://arxiv.org/abs/1907.09701","internal_url":null},{"position":40,"raw":"[Yeh and Lien, 2009] I-Cheng Yeh and Che-hui Lien. The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients. Expert systems with applications, 36(2):2473-2480, 2009.","title":"The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients","authors":"Lien Yeh","year":"2009","venue":"Expert systems with applications","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Yeh et al., 2019] Chih-Kuan Yeh, Cheng-Yu Hsieh, Arun Suggala, David I Inouye, and Pradeep K Ravikumar. On the (In)fidelity and Sensitivity of Explanations. In Ad- vances in Neural Information Processing Systems, vol- ume 32.","title":"On the (In)fidelity and Sensitivity of Explanations","authors":"Chih-Kuan Yeh, Cheng-Yu Hsieh, Arun Suggala, David I Inouye, Pradeep K Ravikumar","year":null,"venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"Curran Associates, Inc., 2019.","title":null,"authors":"Curran Associates, Inc","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Zhou et al., 2021] Jianlong Zhou, Amir H. Gandomi, Fang Chen, and Andreas Holzinger. Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics, 10(5), 2021.","title":"Evaluating the quality of machine learning explanations: A survey on methods and metrics","authors":"Jianlong Zhou, H Amir, Fang Gandomi, Andreas Chen, Holzinger","year":"2021","venue":"Electronics","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}