AI Pilot in the Cockpit: An Investigation of Public Acceptance

Abstract

Crew-reducing exhibits promise for various benefits in the aviation industry. However, there is limited understanding regarding public acceptance. Using an experimental design deployed with a vignette-based online study, we investigated individuals’ negative emotion, trust, risk acceptance, and willingness to ride toward Single Pilot Operations (SPO) and Dual Pilot Operations (DPO). Results established that people preferred DPO flights, relying on affect heuristic. Specifically, people’s negative emotion associated with SPO decreases their trust, which subsequently results in lower levels of willingness to ride and risk acceptance. Furthermore, we observed that people are less likely to accept risk evoked by intelligent autonomous system in DPO flights, which likely due to their psychological model about two pilots in the cockpit. Findings from this research highlight the importance of users’ initially positive affects about intelligent equipment in the cockpit. Other theoretical and practical implications for narrowing the acceptable gap between SPO and DPO are discussed.

Keywords:

• Single pilot operations (SPO)
• technology acceptance
• emotion
• trust
• mediation model

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author, L.W., upon reasonable request.

Notes

1 The SPO and DPO flights are assumed to have the same safety performance.

2 It depends on the treatment.

3 To ensure translation accuracy, the items were firstly translated into Chinese by a bilingual translator. Secondly, another bilingual translator reviewed the translated items for accuracy and appropriateness. Thirdly, a back-translation was conducted by a third bilingual translator to ensure the accuracy of the translation. Lastly, the back-translated version was compared to the original items to ensure consistency.

4 To ensure the descriptive information we provided are sufficient enough for participants, we selected the Chinese version adapted from Xu et al. (2022).

Additional information

Funding

This study was supported by the Tianjin Research Innovation Project for Postgraduate Students [grant number 2022BKY150].

Notes on contributors

Shan Gao

Shan Gao received the M.Sc. degree in Flight Technology and Safety from Civil Aviation University of China in 2021. He is currently pursuing the Ph.D. degree in Safety Science and Engineering at Civil Aviation University of China. His research interests include human factors and human-computer interaction.

Zhuoran Lu

Zhuoran Lu received the B.Sc. degree in Computer Science and Statistics from University of Illinois at Urbana-Champaign in 2019. He is a Ph.D. student in Computer Science at Purdue University. His research focuses on the intersection of human-computer interaction and artificial intelligence.

Hao Luan

Hao Luan received the B.Sc. degree in Transportation and Logistics from Chongqing Jiaotong University in 2021. He is a graduate student in Transportation Engineering at Civil Aviation University of China. His research focuses on human-automation interaction.

Ming Yin

Ming Yin received the Ph.D.degree in computer science from Harvard University in 2017. She is currently an assistant professor in the Department of Computer Science, Purdue University. Her research interests include human-AI interaction, crowdsourcing and human computation, and computational social science.

Lei Wang

Lei Wang received the Ph.D. degree in Applied Psychology from Chinese Academy of Sciences in 2014. He is currently a professor with the College of Safety Science and Engineering, Civil Aviation University of China. His research interests include human factors and engineering psychology.