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引用“”的文章Guidelines for Human-AI Interaction

标题/作者	年份
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
Human-centered artificial intelligence: Reliable, safe & trustworthy B Shneiderman International Journal of Human–Computer Interaction 36 (6), 495-504	2020
" Hello AI": uncovering the onboarding needs of medical practitioners for human-AI collaborative decision-making CJ Cai, S Winter, D Steiner, L Wilcox, M Terry Proceedings of the ACM on Human-computer Interaction 3 (CSCW), 1-24	2019
Co-designing checklists to understand organizational challenges and opportunities around fairness in AI MA Madaio, L Stark, J Wortman Vaughan, H Wallach Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems …	2020
Human-centred learning analytics S Buckingham Shum, R Ferguson, R Martinez-Maldonado Journal of Learning Analytics 6 (2), 1-9	2019
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
Beyond accuracy: The role of mental models in human-AI team performance G Bansal, B Nushi, E Kamar, WS Lasecki, DS Weld, E Horvitz Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 7 …	2019
Bridging the gap between ethics and practice: guidelines for reliable, safe, and trustworthy human-centered AI systems B Shneiderman ACM Transactions on Interactive Intelligent Systems (TiiS) 10 (4), 1-31	2020
explAIner: A visual analytics framework for interactive and explainable machine learning T Spinner, U Schlegel, H Schäfer, M El-Assady IEEE transactions on visualization and computer graphics 26 (1), 1064-1074	2019
History and future of human-automation interaction CP Janssen, SF Donker, DP Brumby, AL Kun International Journal of Human-Computer Studies 131, 99-107	2019
How do data science workers collaborate? roles, workflows, and tools AX Zhang, M Muller, D Wang Proceedings of the ACM on Human-Computer Interaction 4 (CSCW1), 1-23	2020
Proxy tasks and subjective measures can be misleading in evaluating explainable AI systems Z Buçinca, P Lin, KZ Gajos, EL Glassman Proceedings of the 25th international conference on intelligent user …	2020
Trust in AutoML: exploring information needs for establishing trust in automated machine learning systems J Drozdal, J Weisz, D Wang, G Dass, B Yao, C Zhao, M Muller, L Ju, H Su Proceedings of the 25th International Conference on Intelligent User …	2020
Novice-AI music co-creation via AI-steering tools for deep generative models R Louie, A Coenen, CZ Huang, M Terry, CJ Cai Proceedings of the 2020 CHI conference on human factors in computing systems …	2020
Explainable artificial intelligence: Concepts, applications, research challenges and visions L Longo, R Goebel, F Lecue, P Kieseberg, A Holzinger International Cross-Domain Conference for Machine Learning and Knowledge …	2020
Rapid trust calibration through interpretable and uncertainty-aware AI R Tomsett, A Preece, D Braines, F Cerutti, S Chakraborty, M Srivastava, ... Patterns 1 (4), 100049	2020
Artificial intelligence for social good: A survey ZR Shi, C Wang, F Fang arXiv preprint arXiv:2001.01818	2020
COGAM: measuring and moderating cognitive load in machine learning model explanations A Abdul, C von der Weth, M Kankanhalli, BY Lim Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems …	2020
Human-centric AI for trustworthy IoT systems with explainable multilayer perceptrons I Garcia-Magarino, R Muttukrishnan, J Lloret IEEE Access 7, 125562-125574	2019
A Human-in-the-loop Perspective on AutoML: Milestones and the Road Ahead DJL Lee, S Macke IEEE Data Engineering Bulletin	2020

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