Explanations and trust: What happens to trust when a robot partner does something unexpected?

Lyons, Joseph B.; Hamdan, Izz Aldin; Vo, Thy

doi:10.1016/j.chb.2022.107473

Cited by 35 publications

(14 citation statements)

References 37 publications

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“…This implies that promises restore trustworthiness through increasing benevo- lence. This finding echoes other findings from the HRI liter- ature more broadly (Esterwood & Robert, 2021, 2023; Lyons et al, 2023). This finding can be used to inform future the- oretical development related to trust repair.…”

Section: Discussion and Future Worksupporting

confidence: 88%

“…Benevolence –sometimes referred to as purpose– is the extent to which the trustee is perceived as caring for the trustor’s best interests and acting accordingly (Mayer et al, 1995). In summary, the three sub- components of trustworthiness – ability, integrity, and benevo- lence – each make up trustworthiness, which in turn, influences one’s willingness to bestow trust not only to humans but also to robots (Esterwood & Robert, 2023; Kim et al, 2020; Lyons, aldin Hamdan, & Vo, 2023; Sebo et al, 2019).…”

Section: Related Workmentioning

confidence: 99%

“…trust violations– can have dire con- sequences. In particular, recent examples across the human- factors literature have highlighted how trust violations can neg- atively impact not only trust (Azevedo-Sa et al, 2020; Hancock et al, 2011; Lyons et al, 2023) but also acceptance (Seet et al, 2020) and task performance – in terms of secondary task – (Azevedo-Sa et al, 2020; Chen & Terrence, 2009) along- side takeover performance (Körber, Baseler, & Bengler, 2018; Payre, Cestac, & Delhomme, 2016). In light of these findings, it becomes increasingly important to consider how trust repairs might be capable of mitigating these negative consequences.…”

Section: Related Workmentioning

confidence: 99%

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Promises and Trust Repair in UGVs

Esterwood,

Ali,

George

et al. 2023

Proceedings of the Human Factors and Ergonomics Society Annual

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Unmanned ground vehicles (UGVs) are autonomous robots capable of performing tasks through self- navigation and decision-making. They have the potential to replace humans in dangerous driving scenarios. However, UGVs must be viewed as trustworthy to be accepted, and like any automation, they can make mistakes that decrease human trust in them. Trust repair strategies can mitigate the consequences of trust violations, but they are not always effective. To better understand their effectiveness on UGVs, we designed a between-subjects study examining promises on a UGV’s trustworthiness. Preliminary results showed that promises had a marginal impact on overall trustworthiness but were influential in repairing benevolence but not ability or integrity. These findings have implications for the design of UGV’s and trust repair theory.

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Section: Discussion and Future Worksupporting

confidence: 88%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Promises and Trust Repair in UGVs

Esterwood,

Ali,

George

et al. 2023

Proceedings of the Human Factors and Ergonomics Society Annual

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“…People may trust other people more simply because they are familiar and understood, while AI is still somewhat unknown and potentially mistrusted. In a study investigating a robot teammate's unexpected behavior, the results indicated a decrease in both trust and trustworthiness after the behavioral violation, leading to a shift in responsibility attribution from the human to the robot (Lyons et al, 2023).…”

Section: Discussionmentioning

confidence: 99%

Caution with AI deception: AI could serve as the scapegoat of Human!

Wu,

Xu,

Xuegang

et al. 2024

Preprint

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In the era of widespread artificial intelligence (AI), it is crucial to understand the distinct roles of AI and humans in deception. This study employs an innovative empirical approach to investigate how deceived responses extend from human deception to artificial intelligence deception. We conducted an experiment using virtual scenario manipulations to explore how individuals perceive and respond to deception when humans and AI act as solo or collusive deceptive agents. The results indicate that when both humans and AI engage in deception under false propositions, AI is perceived as more deceptive and intentional than humans, making it more likely to bear the blame and receive reduced trust. The willingness to communicate with AI also experiences a decline. The perception of intent to deceive serves as a mediating factor in the connection between the agent (human or AI) and the attribution of blame or trust, rather than the presence of actual deception. This study provides empirical evidence for understanding AI deception and highlights the ethical and moral concerns when humans collaborate with AI and depend on AI-generated information.

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“…This would be consistent with Dignum ( 2017 ) Functional level of moral competence. Such methods might include the ability to communicate in relation to norms and to engage in trust repair and explanation when behaviors violate norms (Malle and Scheutz, 2014 ; Lyons et al, 2023 ). A recent study by Lyons et al ( 2023 ) examined human reactions to a situation where a robot violated a behavioral expectation for a robot to follow a plan issued by a human operator.…”

Section: Introductionmentioning

confidence: 99%

Responsible (use of) AI

Lyons,

Hobbs,

Rogers

et al. 2023

Front. Neuroergonomics

Self Cite

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Although there is a rich history of philosophical definitions of ethics when applied to human behavior, applying the same concepts and principles to AI may be fraught with problems. Anthropomorphizing AI to have characteristics such as “ethics” may promote a dangerous, unrealistic expectation that AI can be trained to have inherent, guaranteed ethical behavior. The authors instead advocate for increased research into the ethical use of AI from initial ideation and design through operational use and sustainment. The authors advocate for five key research areas: (1) education in ethics and core AI concepts for AI developers, leaders, and users, (2) development and use of model cards or datasheets for datasets to provide transparency into the strengths, limits, and potential biases of a trained model, (3) employing human-centered design that seeks to understand human value structures within a task context and enable effective human-machine interaction through intuitive and transparent interfaces, (4) targeted use of run time assurance that monitors and modifies the inputs or outputs of a trained model when necessary to enforce ethical principles such as safety or limiting bias, and (5) developing best practices for the use of a joint human-AI co-creation and training experience to enable a shared mental model and higher performance through potential emergent behavior.

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Explanations and trust: What happens to trust when a robot partner does something unexpected?

Cited by 35 publications

References 37 publications

Promises and Trust Repair in UGVs

Promises and Trust Repair in UGVs

Caution with AI deception: AI could serve as the scapegoat of Human!

Responsible (use of) AI

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