Human-Centered Explainability for Intelligent Vehicles—A User Study

References

• Abdul, A., Vermeulen, J., Wang, D., Lim, B. Y., & Kankanhalli, M. (2018). Trends and trajectories for explainable, accountable and intelligible systems. In R. Mandryk, M. Hancock, M. Perry, & A. Cox (Eds.), Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (pp. 1–18). ACM. https://doi.org/10.1145/3173574.3174156
   Google Scholar
• Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). IEEE Access, 6, 52138–52160. https://doi.org/10.1109/ACCESS.2018.2870052
   Web of Science ®Google Scholar
• Amershi, S., Weld, D., Vorvoreanu, M., Fourney, A., Nushi, B., Collisson, P., Suh, J., Iqbal, S., Bennett, P. N., Inkpen, K., Teevan, J., Kikin-Gil, R., & Horvitz, E. (2019). Guidelines for human–AI interaction. In S. Brewster, G. Fitzpatrick, A. Cox, & V. Kostakos (Eds.), Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1–13). ACM. https://doi.org/10.1145/3290605.3300233
   Google Scholar
• Apple Inc. (2021). Human interface guidelines–Machine learning. Retrieved from https://developer.apple.com/design/human-interface-guidelines/machine-learning/overview/introduction/
   Google Scholar
• Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012
   Web of Science ®Google Scholar
• Chandrashekar, A., Amat, F., Basilico, J., & Jebara, T. (2017). Artwork personalization at Netflix. Retrieved from https://netflixtechblog.com/artwork-personalization-c589f074ad76
   Google Scholar
• Dorneich, M. C., McGrath, K. A., Dudley, R. F., & Morris, M. D. (2013). Analysis of the characteristics of adaptive systems. In IEEE International Conference on Systems, Man, and Cybernetics (pp. 888–893). https://doi.org/10.1109/SMC.2013.156
   Google Scholar
• Doshi-Velez, F., & Kim, B. (2017, February 28). Towards a rigorous science of interpretable machine learning. Retrieved from http://arxiv.org/pdf/1702.08608v2
   Google Scholar
• Eiband, M., Völkel, S. T., Buschek, D., Cook, S., & Hussmann, H. (2019). When people and algorithms meet. In W.-T. Fu, S. Pan, O. Brdiczka, P. Chau, & G. Calvary (Eds.), Proceedings of the 24th International Conference on Intelligent User Interfaces (pp. 96–106). ACM. https://doi.org/10.1145/3301275.3302262
   Google Scholar
• Eslami, M., Rickman, A., Vaccaro, K., Aleyasen, A., Vuong, A., Karahalios, K., Hamilton, K., & Sandvig, C. (2015). I always assumed that I wasn’t really that close to [her]. In B. Begole, J. Kim, K. Inkpen, & W. Woo (Eds.), Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (pp. 153–162). ACM. https://doi.org/10.1145/2702123.2702556
   Google Scholar
• Feigh, K. M., Dorneich, M. C., & Hayes, C. C. (2012). Toward a characterization of adaptive systems: A framework for researchers and system designers. Human Factors, 54(6), 1008–1024. https://doi.org/10.1177/0018720812443983
   PubMed Web of Science ®Google Scholar
• Google PAIR (2019). People + AI Guidebook. Retrieved from https://pair.withgoogle.com/guidebook/
   Google Scholar
• Google (n.d.). Find places you’ll like. Retrieved February 10, 2022, from https://support.google.com/maps/answer/7677966?hl=en&co=GENIE.Platform%3DAndroid#zippy=%2Chow-matches-are-created%2Chow-ratings-affect-your-matchest
   Google Scholar
• Graefe, J., Engelhardt, D., Rittger, L., & Bengler, K. (2022). How well does the algorithm know me? In M. M. Soares, E. Rosenzweig, & A. Marcus (Eds.), Lecture notes in computer science. Design, user experience, and usability: Design thinking and practice in contemporary and emerging technologies (Vol. 13323, pp. 311–336). Springer International Publishing. https://doi.org/10.1007/978-3-031-05906-3_24
   Google Scholar
• Holzinger, A., Langs, G., Denk, H., Zatloukal, K., & Müller, H. (2019). Causability and explainability of artificial intelligence in medicine. Wiley Interdisciplinary Reviews. Data Mining and Knowledge Discovery, 9(4), e1312. https://doi.org/10.1002/widm.1312
   PubMed Web of Science ®Google Scholar
• Jameson, A., & Gajos, K. Z. (2012). Systems that adapt to their users. In J. A. Jacko (Ed.), Human factors and ergonomics. The human–computer interaction handbook: Fundamentals, evolving technologies, and emerging applications (3rd ed., pp. 431–455). Taylor & Francis.
   Google Scholar
• Köhler, L. M. (2018). Adaptives Informationskonzept für beanspruchende urbane Fahrsituationen [PhD thesis]. Technical University of Munich.
   Google Scholar
• Koo, J., Kwac, J., Ju, W., Steinert, M., Leifer, L., & Nass, C. (2015). Why did my car just do that? Explaining semi-autonomous driving actions to improve driver understanding, trust, and performance. International Journal on Interactive Design and Manufacturing, 9(4), 269–275. https://doi.org/10.1007/s12008-014-0227-2
   Google Scholar
• Körber, M. (2019). Theoretical considerations and development of a questionnaire to measure trust in automation. In S. Bagnara, R. Tartaglia, S. Albolino, T. Alexander, & Y. Fujita (Eds.), Advances in Intelligent Systems and Computing. Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018) (Vol. 823, pp. 13–30). Springer International Publishing. https://doi.org/10.1007/978-3-319-96074-6_2
   Google Scholar
• Lim, B. Y., & Dey, A. K. (2010). Toolkit to support intelligibility in context-aware applications. In J. E. Bardram, M. Langheinrich, K. N. Truong, & P. Nixon (Eds.), Proceedings of the 12th ACM International Conference on Ubiquitous Computing (pp. 13–22). ACM. https://doi.org/10.1145/1864349.1864353
   Google Scholar
• Lim, B. Y., & Dey, A. K. (2013). Evaluating intelligibility usage and usefulness in a context-aware application. In D. Hutchison, T. Kanade, J. Kittler, J. M. Kleinberg, F. Mattern, J. C. Mitchell, M. Naor, O. Nierstrasz, C. Pandu Rangan, B. Steffen, M. Sudan, D. Terzopoulos, D. Tygar, M. Y. Vardi, G. Weikum, & M. Kurosu (Eds.), Lecture notes in computer science. Human–computer interaction. Towards intelligent and implicit interaction (Vol. 8008, pp. 92–101). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-39342-6_11
   Google Scholar
• Lim, B. Y., Dey, A. K., & Avrahami, D. (2009). Why and why not explanations improve the intelligibility of context-aware intelligent systems. In D. R. Olsen, R. B. Arthur, K. Hinckley, M. R. Morris, S. Hudson, & S. Greenberg (Eds.), Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 2119–2128). ACM. https://doi.org/10.1145/1518701.1519023
   Google Scholar
• Markus, A. F., Kors, J. A., & Rijnbeek, P. R. (2021). The role of explainability in creating trustworthy artificial intelligence for health care: A comprehensive survey of the terminology, design choices, and evaluation strategies. Journal of Biomedical Informatics, 113, 103655. https://doi.org/10.1016/j.jbi.2020.103655
   PubMed Web of Science ®Google Scholar
• Meta (Ed.). (2021). How machine learning powers Facebook’s News Feed ranking algorithm. Retrieved from https://engineering.fb.com/2021/01/26/ml-applications/news-feed-ranking/
   Google Scholar
• Miller, T. (2019). Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence, 267, 1–38. https://doi.org/10.1016/j.artint.2018.07.007
   Web of Science ®Google Scholar
• Mohseni, S., Zarei, N., & Ragan, E. D. (2021). A multidisciplinary survey and framework for design and evaluation of explainable AI systems. ACM Transactions on Interactive Intelligent Systems, 11(3–4), 1–45. https://doi.org/10.1145/3387166
   Web of Science ®Google Scholar
• Molnar, C. (2019). Interpretable machine learning. A guide for making black box models explainable. Retrieved from https://christophm.github.io/interpretable-ml-book/
   Google Scholar
• Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000). A model for types and levels of human interaction with automation. IEEE Transactions on Systems, Man, and Cybernetics. Part A, Systems and Humans, 30(3), 286–297. https://doi.org/10.1109/3468.844354
   PubMed Web of Science ®Google Scholar
• Rader, E., Cotter, K., & Cho, J. (2018). Explanations as mechanisms for supporting algorithmic transparency. In R. Mandryk, M. Hancock, M. Perry, & A. Cox (Eds.), Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (pp. 1–13). ACM. https://doi.org/10.1145/3173574.3173677
   Google Scholar
• Ribera, M., & Lapedriza, A. (2019). Can we do better explanations? A proposal of user-centered explainable AI. In ACM (Chair), ACM IUI.
   Google Scholar
• Rittger, L., Engelhardt, D., & Schwartz, R. (2022). Adaptive user experience in the car—Levels of adaptivity and adaptive HMI design. IEEE Transactions on Intelligent Transportation Systems, 23(5), 4866–4876. https://doi.org/10.1109/TITS.2021.3124990
   Web of Science ®Google Scholar
• Schneider, T., Hois, J., Rosenstein, A., Ghellal, S., Theofanou-Fülbier, D., & Gerlicher, A. R. (2021). ExplAIn yourself! Transparency for positive UX in autonomous driving. In Y. Kitamura, A. Quigley, K. Isbister, T. Igarashi, P. Bjørn, & S. Drucker (Eds.), Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1–12). ACM. https://doi.org/10.1145/3411764.3446647
   Google Scholar
• Semmens, R., Martelaro, N., Kaveti, P., Stent, S., & Ju, W. (2019). Is now a good time? In S. Brewster, G. Fitzpatrick, A. Cox, & V. Kostakos (Eds.), Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1–12). ACM. https://doi.org/10.1145/3290605.3300867
   Google Scholar
• Shin, D. (2021). The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI. International Journal of Human–Computer Studies, 146, 102551. https://doi.org/10.1016/j.ijhcs.2020.102551
   Web of Science ®Google Scholar
• Tchankue, P., Wesson, J., & Vogts, D. (2011). The impact of an adaptive user interface on reducing driver distraction. In M. Tscheligi (Ed.), Proceedings of the 3rd International Conference on Automotive User Interfaces and Interactive Vehicular Applications–AutomotiveUI '11 (p. 87). ACM Press. https://doi.org/10.1145/2381416.2381430
   Google Scholar
• Tomsett, R., Preece, A., Braines, D., Cerutti, F., Chakraborty, S., Srivastava, M., Pearson, G., & Kaplan, L. (2020). Rapid trust calibration through interpretable and uncertainty-aware AI. Patterns, 1(4), 100049. https://doi.org/10.1016/j.patter.2020.100049
   PubMedGoogle Scholar
• van der Laan, J. D., Heino, A., & de Waard, D. (1997). A simple procedure for the assessment of acceptance of advanced transport telematics. Transportation Research Part C: Emerging Technologies, 5(1), 1–10. https://doi.org/10.1016/S0968-090X(96)00025-3
   Web of Science ®Google Scholar
• Völkel, S. T., Schneegass, C., Eiband, M., & Buschek, D. (2020). What is “intelligent” in intelligent user interfaces? In F. Paternò, N. Oliver, C. Conati, L. D. Spano, & N. Tintarev (Eds.), Proceedings of the 25th International Conference on Intelligent User Interfaces (pp. 477–487). ACM. https://doi.org/10.1145/3377325.3377500
   Google Scholar
• Walter, N. (2018). Personalization and context-sensitive user interaction of in-vehicle infotainment systems [PhD thesis]. Technische Universität München.
   Google Scholar
• Wintersberger, P., Nicklas, H., Martlbauer, T., Hammer, S., & Riener, A. (2020). Explainable automation: Personalized and adaptive UIs to foster trust and understanding of driving automation systems. In 12th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (pp. 252–261). ACM. https://doi.org/10.1145/3409120.3410659
   Google Scholar
• Wright, A. P., Wang, Z. J., Park, H., Guo, G., Sperrle, F., El-Assady, M., Endert, A., Keim, D., & Chau, D. H. (2020). A comparative analysis of industry human–AI interaction guidelines. Retrieved from http://arxiv.org/pdf/2010.11761v1
   Google Scholar