Effects of explanation types and perceived risk on trust in autonomous vehicles

Ha, Taehyun; Kim, Sangyeon; Seo, Dae Gyo; Lee, Sang‐Won

doi:10.1016/j.trf.2020.06.021

Cited by 95 publications

(62 citation statements)

References 18 publications

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“…To this end, they developed a real-time trust measurement to explore trust levels according to different driving scenarios. Similarly, Ha et al (2020) examine the impact of explanation types and perceived risk on trust in AVs. Simple explanations, or feedback, such as descriptions of the vehicle's tasks, led to elevated trust in AVs, while too much explanation led to potential cognitive overload, and did not increase trust (Ha et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

A Systematic Review of Virtual Reality Applications for Automated Driving: 2009–2020

2021

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While virtual reality (VR) interfaces have been researched extensively over the last decades, studies on their application in vehicles have only recently advanced. In this paper, we systematically review 12 years of VR research in the context of automated driving (AD), from 2009 to 2020. Due to the multitude of possibilities for studies with regard to VR technology, at present, the pool of findings is heterogeneous and non-transparent. We investigated N = 176 scientific papers of relevant journals and conferences with the goal to analyze the status quo of existing VR studies in AD, and to classify the related literature into application areas. We provide insights into the utilization of VR technology which is applicable at specific level of vehicle automation and for different users (drivers, passengers, pedestrians) and tasks. Results show that most studies focused on designing automotive experiences in VR, safety aspects, and vulnerable road users. Trust, simulator and motion sickness, and external human-machine interfaces (eHMIs) also marked a significant portion of the published papers, however a wide range of different parameters was investigated by researchers. Finally, we discuss a set of open challenges, and give recommendation for future research in automated driving at the VR side of the reality-virtuality continuum.

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Section: Discussionmentioning

confidence: 99%

A Systematic Review of Virtual Reality Applications for Automated Driving: 2009–2020

2021

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“…Merely stating the AI's performance without a detailed explanation seemed insufficient to justify its limitations and algorithmic certainty. Previous studies that focused on system uncertainty or algorithmic limitations found that such information has a detrimental effect on users' trust (Cai et al, 2019;Lim & Dey, 2011;Papenmeier et al, 2019;Robinette, Howard, & Wagner, 2017;Ha, Kim, Seo, & Lee, 2020). For example, Robinette, Howard, and Wagner (2017) showed that when people find themselves in a highrisk situation, they lose trust in the computer's advice, which reveals its own limitations.…”

Section: Cost Of Providing Information About the Performance Of Aimentioning

confidence: 99%

“…For example, Robinette, Howard, and Wagner (2017) showed that when people find themselves in a highrisk situation, they lose trust in the computer's advice, which reveals its own limitations. Furthermore, Ha, Kim, Seo, and Lee (2020) suggested that people trust AI more when it provides no information about itself than when information is provided.…”

Section: Cost Of Providing Information About the Performance Of Aimentioning

confidence: 99%

Communicating the Limitations of AI: The Effect of Message Framing and Ownership on Trust in Artificial Intelligence

Kim¹,

Song²

2021

Preprint

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Trust plays an essential role in the interaction between humans and artificial intelligence (AI). While too much trust could harm the interaction between humans and AI, the lack of trust in AI could increase users’ reluctance to use the system. One way to foster trust in AI is to provide information about the performance. However, the manner in which the information is presented, such as message framing and the ownership of decisions, could also affect the trust. To test this, we conducted a 2 (ownership: no ownership vs. ownership) × 3 (message framing: no information vs. negative information vs. positive information) between-subjects experiment. Participants were required to choose items for survival in the desert supported by AI decision aid. The results demonstrated that participants trusted AI more willingly in the absence of any information about the performance and when they did not have ownership of their decisions. Implications are discussed.

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“…Post-hoc explanations provided on this premise significantly increased the user's level of situation awareness. Ha et al [14] and Koo et al [15] examined the effect of explanations on peoples' trust through user studies. Ha et al [14] examined two explanation types, simple and attributional, as well as perceived risk on trust in AVs in four autonomous driving scenarios with different levels of risk.…”

Section: Related Workmentioning

confidence: 99%

“…Ha et al [14] and Koo et al [15] examined the effect of explanations on peoples' trust through user studies. Ha et al [14] examined two explanation types, simple and attributional, as well as perceived risk on trust in AVs in four autonomous driving scenarios with different levels of risk. Their results show that the explanation type significantly affects trust in autonomous vehicles and that, under high levels of perceived risk, attributional explanations lead to the highest trust.…”

Section: Related Workmentioning

confidence: 99%

Why Not Explain? Effects of Explanations on Human Perceptions of Autonomous Driving

Omeiza

Kollnig

Web

et al. 2021

2021 IEEE International Conference on Advanced Robotics and Its Social Impacts (ARSO)

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Autonomous vehicles (AVs) have the potential to change the way we commute, travel, and transport our goods. The deployment of AVs in society, however, requires that people understand, accept, and trust them. Intelligible explanations can help different AV stakeholders to assess AVs' behaviours, and in turn, increase their confidence and foster trust. In a user study (N = 101), we examined different explanation types (based on investigatory queries) provided by an AV and their effect on people using the trust determinant factors. Our quantitative and qualitative analysis shows that explanations with causal attributions improved task performance and understanding when assessing driving events but did not directly improve perceived trust. This underlines the potential need for additional measures and research to enhance trust in AVs.

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Effects of explanation types and perceived risk on trust in autonomous vehicles

Cited by 95 publications

References 18 publications

A Systematic Review of Virtual Reality Applications for Automated Driving: 2009–2020

A Systematic Review of Virtual Reality Applications for Automated Driving: 2009–2020

Communicating the Limitations of AI: The Effect of Message Framing and Ownership on Trust in Artificial Intelligence

Why Not Explain? Effects of Explanations on Human Perceptions of Autonomous Driving

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