Abstract
This study evaluates people’s attitude and preferences toward human-machine interaction from a machine ethics perspective. An interview was first conducted with 30 participants to gather ideas and concerns about future AI technology. Then a survey was conducted with 103 participants to collect quantitative data, and an in-depth interview held with 30 participants to support and provide insights to the questionnaire results. It revealed that severity, time and relativity have significant impacts on people’s choices over automation level, decision-making approach, and responsibility allocation. Either monitored control, consensual control, or both, were the selected as the most preferred automation levels in the different scenarios as opposed to manual control and full automation. The results of this study indicated that AI technology should be adaptively designed to suit specific situations with different combinations of influence factors.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Thomson, J.J.: The trolley problem. Yale Law J. 94, 1395–1415 (1985). https://doi.org/10/c2rrks
Nyholm, S., Smids, J.: The ethics of accident-algorithms for self-driving cars: an applied trolley problem? Ethical Theor. Moral Pract. 19, 1275–1289 (2016)
Parasuraman, R., Sheridan, T.B., Wickens, C.D.: A model for types and levels of human interaction with automation. IEEE Trans. Syst. Man Cybern.-Part A: Syst. Hum. 30, 286–297 (2000)
Mosier, K.L., Skitka, L.J.: Human decision makers and automated decision aids: Made for each other? In: Automation and Human Performance, pp. 201–220. Routledge (2018)
Rau, P.-L.P., Gong, Y., Dai, Y.-B., Cheng, C.: Promote energy conservation in automatic environment control: a comfort-energy trade-off perspective. In: Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems, pp. 1501–1506 (2015)
Sheridan, T.B., Verplank, W.L.: Human and computer control of undersea teleoperators. Massachusetts Inst of Tech Cambridge Man-Machine Systems Lab (1978)
Riley, V.: A general model of mixed-initiative human-machine systems. In: Proceedings of the Human Factors Society Annual Meeting, pp. 124–128. SAGE Publications Sage CA, Los Angeles, CA (1989)
Endsley, M., Kaber, D.: Level of automation eŒ ects on performance, situation awareness and workload in a dynamic control task. Ergonomics 42, 462–492 (1999). https://doi.org/10/fk54hd
Anderson, M., Anderson, S.L.: Machine ethics: Creating an ethical intelligent agent. AI Mag. 28, 15 (2007)
Bostrom, N., Yudkowsky, E.: The ethics of artificial intelligence. Camb. Handb. Artif. Intell. 1, 316–334 (2014). https://doi.org/10.1017/CBO9781139046855.020
Hastie, T., Tibshirani, R., Friedman, J.: The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media (2009)
McConnell, T.: Moral dilemmas (2002)
McConnell, T.C.: Moral dilemmas and consistency in ethics. Can. J. Philos. 8, 269–287 (1978). https://doi.org/10/ggwxfm
Markkula Center for Applied Ethics: A Framework for Ethical Decision Making, https://www.scu.edu/ethics/ethics-resources/ethical-decision-making/a-framework-for-ethical-decision-making/. Accessed 23 May 2020
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Sun, H., Rau, PL.P., Wang, B. (2021). A Study of Machine Ethics in Human-Artificial Intelligence Interactions. In: Rau, PL.P. (eds) Cross-Cultural Design. Applications in Arts, Learning, Well-being, and Social Development. HCII 2021. Lecture Notes in Computer Science(), vol 12772. Springer, Cham. https://doi.org/10.1007/978-3-030-77077-8_29
Download citation
DOI: https://doi.org/10.1007/978-3-030-77077-8_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-77076-1
Online ISBN: 978-3-030-77077-8
eBook Packages: Computer ScienceComputer Science (R0)