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.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Chen, J.Y.C., Procci, K., Boyce, M., Wright, J., Garcia, A., Barnes, M.: Situation awareness–based agent transparency. Technical report, Army Research Laboratory ARL-TR-6905 (2014)
Das, S., Dey, A., Pal, A., Roy, N.: Applications of artificial intelligence in machine learning: review and prospect. Int. J. Comput. Appl. 115(9), 31–41 (2015)
Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: a survey. In: Proceedings of the 41st International Convention on Information and Communication Technology, Electronics and Microelectronics MIPRO, pp. 210–215. IEEE Xplore (2018)
Endsley, M.R.: Toward a theory of situation awareness in dynamic systems. Hum. Factors J. 37(1), 32–64 (1995)
Fitts, P.M.: Human engineering for an effective air navigation and traffic control system. Technical report, National Research Council (1951)
Gunning, D.: Explainable artificial intelligence (XAI). DARPA Program (2017). https://www.darpa.mil/program/explainable-artificial-intelligence. Accessed 18 Mar 2019
Gil, Y., Selman, B.: A 20-year community roadmap for artificial intelligence research in the US executive summary. https://cra.org/ccc/wp-content/uploads/sites/2/2019/03/AI_Roadmap_Exec_Summary-FINAL-.pdf. Accessed 18 Mar 2019
Holliday, D., Wilson, S., Stumpf, S.: User trust in intelligent systems: a journey over time. In: Proceedings of the 21st International Conference on Intelligent User Interfaces, pp. 164–168. ACM, New York (2016)
Karapanos, E., Zimmerman, J., Forlizzi, J., Martens, J.-B.: User experience over time: an initial framework. In: CHI 2009 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 729–738. ACM, New York (2019)
Lipton, Z.C.: The mythos of model interpretability. Commun. ACM 61(10), 36–43 (2016)
Miller, T.: Explanation in artificial intelligence: insights from the social sciences. Artif. Intell. J. 267, 1–38 (2019)
Mohseni, S., Zarei, N., Ragan, E.D.: A survey of evaluation methods and measures for interpretable machine learning. Computing Research Repository (CoRR) (2018). http://arxiv.org/abs/1811.11839. Accessed 18 Mar 2019
Parasuraman, R., Sheridan, T.B., Wickens, C.D.: Model for types and levels of human interaction with automation. IEEE Trans. Syst. Man Cybern. – Part A: Syst. Hum. 30, 286–297 (2000)
Ribeiro, M.T., Singh, S., Guestrin, C.: “Why should i trust you?” Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135–1144. ACM, New York (2016)
Samek, W., Wiegand, T., Müller, K.-R.: Explainable artificial intelligence: understanding, visualizing and interpreting deep learning models. ITU J.: ICT Discov. Impact Artif. Intell. (AI) Commun. Netw. Serv. 1(1), 39–48 (2017)
Schaefer, K.E., Chen, J.Y.C., Szalma, J.L., Hancock, P.A.: A meta-analysis of factors influencing the development of trust in automation: implications for understanding autonomy in future systems. Hum. Factors: J. Hum. Factors Ergon. Soc. 58(3), 377–400 (2016)
Sheridan, T.B., Verplank, W.: Human and computer control of undersea teleoperators. Man-Machine Systems Laboratory, Department of Mechanical Engineering, MIT, USA (1978)
Štrumbelj, E., Kononenko, I.: Explaining prediction models and individual predictions with feature contributions. Knowl. Inf. Syst. 41(3), 647–665 (2014)
Wagner, A., Robinette, P.: Towards robots that trust: human subject validation of the situational conditions for trust. Interact. Stud. 16(1), 89–117 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-23528-4_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-23527-7
Online ISBN: 978-3-030-23528-4
eBook Packages: Computer ScienceComputer Science (R0)