标题/作者	年份
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI AB Arrieta, N Díaz-Rodríguez, J Del Ser, A Bennetot, S Tabik, A Barbado, ... Information fusion 58, 82-115	2020
Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy YK Dwivedi, L Hughes, E Ismagilova, G Aarts, C Coombs, T Crick, Y Duan, ... International Journal of Information Management 57, 101994	2021
Explainable ai: A review of machine learning interpretability methods P Linardatos, V Papastefanopoulos, S Kotsiantis Entropy 23 (1), 18	2020
Machine learning interpretability: A survey on methods and metrics DV Carvalho, EM Pereira, JS Cardoso Electronics 8 (8), 832	2019
Gnnexplainer: Generating explanations for graph neural networks Z Ying, D Bourgeois, J You, M Zitnik, J Leskovec Advances in neural information processing systems 32	2019
Machine learning and deep learning C Janiesch, P Zschech, K Heinrich Electronic Markets 31 (3), 685-695	2021
Explainable machine learning for scientific insights and discoveries R Roscher, B Bohn, MF Duarte, J Garcke Ieee Access 8, 42200-42216	2020
Industry 5.0: A survey on enabling technologies and potential applications PKR Maddikunta, QV Pham, B Prabadevi, N Deepa, K Dev, TR Gadekallu, ... Journal of Industrial Information Integration 26, 100257	2022
Opportunities and challenges in explainable artificial intelligence (xai): A survey A Das, P Rad arXiv preprint arXiv:2006.11371	2020
Questioning the AI: informing design practices for explainable AI user experiences QV Liao, D Gruen, S Miller Proceedings of the 2020 CHI conference on human factors in computing systems …	2020
A survey on the explainability of supervised machine learning N Burkart, MF Huber Journal of Artificial Intelligence Research 70, 245-317	2021
Bias in data‐driven artificial intelligence systems—An introductory survey E Ntoutsi, P Fafalios, U Gadiraju, V Iosifidis, W Nejdl, ME Vidal, S Ruggieri, ... Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 10 (3 …	2020
Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision making Y Zhang, QV Liao, RKE Bellamy Proceedings of the 2020 conference on fairness, accountability, and …	2020
A multidisciplinary survey and framework for design and evaluation of explainable AI systems S Mohseni, N Zarei, ED Ragan ACM Transactions on Interactive Intelligent Systems (TiiS) 11 (3-4), 1-45	2021
Explainable agents and robots: Results from a systematic literature review S Anjomshoae, A Najjar, D Calvaresi, K Främling 18th International Conference on Autonomous Agents and Multiagent Systems …	2019
Interpretable machine learning–a brief history, state-of-the-art and challenges C Molnar, G Casalicchio, B Bischl Joint European conference on machine learning and knowledge discovery in …	2020
The importance of interpretability and visualization in machine learning for applications in medicine and health care A Vellido Neural computing and applications 32 (24), 18069-18083	2020
One explanation does not fit all: A toolkit and taxonomy of ai explainability techniques V Arya, RKE Bellamy, PY Chen, A Dhurandhar, M Hind, SC Hoffman, ... arXiv preprint arXiv:1909.03012	2019
Explainable deep learning: A field guide for the uninitiated G Ras, N Xie, M Van Gerven, D Doran Journal of Artificial Intelligence Research 73, 329-396	2022
A governance model for the application of AI in health care S Reddy, S Allan, S Coghlan, P Cooper Journal of the American Medical Informatics Association 27 (3), 491-497	2020

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