Abstract
It is typically not transparent to end-users, how AI systems derive information or make decisions. This becomes crucial, the more pervasive AI systems enter human daily lives, the more they influence automated decision-making, and the more people rely on them. We present work in progress on explainability to support transparency in human AI interaction. In this paper, we discuss methods and research findings on categorizations of user types, system scope and limits, situational context, and changes over time. Based on these different dimensions and their range and combinations, we aim at individual facets of transparency that address a specific situation best. The approach is human-centered to provide adequate explanations with regard to their depth of detail and level of information, and we outline the different dimensions of this complex task.
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Hois, J., Theofanou-Fuelbier, D., Junk, A.J. (2019). How to Achieve Explainability and Transparency in Human AI Interaction. In: Stephanidis, C. (eds) HCI International 2019 - Posters. HCII 2019. Communications in Computer and Information Science, vol 1033. Springer, Cham. https://doi.org/10.1007/978-3-030-23528-4_25
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DOI: https://doi.org/10.1007/978-3-030-23528-4_25
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