Evaluating the Potential of Interactivity in Explanations for User-Adaptive In-Vehicle Systems – Insights from a Real-World Driving Study-Reference-Cited by-同舟云学术

Evaluating the Potential of Interactivity in Explanations for User-Adaptive In-Vehicle Systems – Insights from a Real-World Driving Study

Published:2023 Issue: Volume: Page:294-312
ISSN:0302-9743
Container-title:HCI International 2023 – Late Breaking Papers
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Author:

Graefe Julia^ORCID,Rittger Lena,Carollo Gabriele,Engelhardt Doreen,Bengler Klaus

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-48047-8_19

Reference37 articles.

1. Abdul, A., Vermeulen, J., Wang, D., et al.: Trends and trajectories for explainable, accountable and intelligible systems. In: Mandryk, R., Hancock, M., Perry, M., et al. (eds.) Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pp. 1–18. ACM, New York (2018)

2. Aizenberg, E., van den Hoven, J.: Designing for human rights in AI. Big Data Soc. 7 (2020). https://doi.org/10.1177/2053951720949566

3. Amershi, S., Weld, D., Vorvoreanu, M., et al.: Guidelines for human-AI interaction. In: Brewster, S., Fitzpatrick, G., Cox, A., et al. (eds.) Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pp. 1–13. ACM, New York (2019)

4. Apple Inc. Human Interface Guidelines - Machine Learning (2021). https://developer.apple.com/design/human-interface-guidelines/machine-learning/overview/introduction/. Accessed 18 June 2021

5. Brennen, A.: What do people really want when they say they want “explainable AI?” We Asked 60 Stakeholders. In: Bernhaupt, R., Mueller, F., Verweij, D., et al. (eds.) Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–7. ACM, New York (2020)