Explainable AI (XAI) Design Cards
The XAI Design Cards offer suggestions and prompts for collecting user-centred AI requirements, user interfaces design suggestions, and strategies for thinking outside the XAI box using the XAIxArts Manifesto.
Created by: N. Bryan-Kinns
Graphic design: Z. Nedaei
Cite as: Bryan-Kinns, N. (2026). Explainable AI Design Cards. https://nickbknickbk.github.io/XAIDesignCards/
Licence: CC BY-NC-SA 4.0 Attribution-NonCommercial-ShareAlike 4.0 International
Supported by: The Bridging Responsible AI Divides programme with funds received from the Arts and Humanities Research Council [grant number AH/X007146/1]
Content for cards drawn from:
Nick Bryan-Kinns, Shuoyang Zheng, Francisco Castro, Makayla Lewis, Jia-Rey Chang, Gabriel Vigliensoni, Terence Broad, Michael Paul Clemens, and Elizabeth Wilson. 2025. XAIxArts Manifesto: Explainable AI for the Arts. In Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA ’25). ACM. https://doi.org/10.1145/3706599.3716227
Q. Vera Liao, Daniel Gruen, and Sarah Miller. 2020. Questioning the AI: Informing Design Practices for Explainable AI User Experiences. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI '20). ACM. https://doi.org/10.1145/3313831.3376590
Meike Nauta, Jan Trienes, Shreyasi Pathak, Elisa Nguyen, Michelle Peters, Yasmin Schmitt, Jörg Schlötterer, Maurice van Keulen, and Christin Seifert. 2023. From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI. ACM Computing Surveys 55, 13s, Article 295. ACM. https://doi.org/10.1145/3583558
Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi. 2018. A Survey of Methods for Explaining Black Box Models. ACM Computing Surveys 51, 5, Article 93. ACM. https://doi.org/10.1145/3236009