Differential biases in human-human versus human-robot interactions

Alarcon, Gene M.; Capiola, August; Hamdan, Izz Aldin; Lee, Michael A.; Jessup, Sarah A.

doi:10.1016/j.apergo.2022.103858

Cited by 15 publications

(6 citation statements)

References 27 publications

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“…The research in this mini-track demonstrates robots can fulfill certain social and emotional aspects of interactions with humans. This helps to satisfy the call from recent research that has noted the importance of non-performance-based aspects of trust in robots, which has received little attention [1]. The current mini-track also expands the literature on moderators of the trust relationship.…”

Section: Introductionmentioning

confidence: 66%

Introduction to the Minitrack on Advances in Trust Research: How Context and Digital Technologies Matter

Möhlmann,

Jarvenpaa,

Alarcon

et al. 2023

Proceedings of the Annual Hawaii International Conference on System Sciences

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Within the Hawaii International Conference onSystem Sciences (HICSS), we organize for the seventh time a mini-track on trust and information technologies. This year, the mini-track on Advances in Trust Research focuses on the context where new digital technologies are developed, brought to markets, implemented, and adopted. The context focus also strives to increase our understanding about trust at different levels of analysis, i.e., individuals, teams, organizations, metaorganizations, and society.

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Section: Introductionmentioning

confidence: 66%

Introduction to the Minitrack on Advances in Trust Research: How Context and Digital Technologies Matter

Möhlmann,

Jarvenpaa,

Alarcon

et al. 2023

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…Human-human relationships are conceived differently from human-automation relationships, where an assessment of trust/distrust seems to be dependent on different factors, see, e.g., ( Jian et al, 2000 ; Alarcon et al, 2023 ; Eicher et al, 2023 ; Zhang et al, 2023 ). Benevolence, for example, is about interpersonal relationships, meaning it might not develop in human-automation relationships the same way it does for human-human relationships ( Centeio Jorge et al, 2021 ).…”

Section: Background and Related Workmentioning

confidence: 99%

“…Trust, however, is not a simple concept. Literature has focused on exploring trust in human-automation teams, particularly looking into the differences between human-human and human-automation trust ( Alarcon et al, 2023 ; Eicher et al, 2023 ; Zhang et al, 2023 ), how this trust can be optimised ( Lee and See, 2004 ; Groom and Nass, 2007 ; Webber, 2008 ; Knocton et al, 2023 ), and which factors reduce trust ( Falcone and Castelfranchi, 2004 ; Madhavan et al, 2006 ; Kopp et al, 2023 ). In particular, automation failure has a significant impact on a person’s trust, i.e., a person that is interacting with the imperfectly reliable has a significantly lower level of trust in it in subsequent interactions ( Robinette et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Exploring the effect of automation failure on the human’s trustworthiness in human-agent teamwork

Centeio Jorge,

Bouman,

Jonker

et al. 2023

Front. Robot. AI

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Introduction: Collaboration in teams composed of both humans and automation has an interdependent nature, which demands calibrated trust among all the team members. For building suitable autonomous teammates, we need to study how trust and trustworthiness function in such teams. In particular, automation occasionally fails to do its job, which leads to a decrease in a human’s trust. Research has found interesting effects of such a reduction of trust on the human’s trustworthiness, i.e., human characteristics that make them more or less reliable. This paper investigates how automation failure in a human-automation collaborative scenario affects the human’s trust in the automation, as well as a human’s trustworthiness towards the automation.Methods: We present a 2 × 2 mixed design experiment in which the participants perform a simulated task in a 2D grid-world, collaborating with an automation in a “moving-out” scenario. During the experiment, we measure the participants’ trustworthiness, trust, and liking regarding the automation, both subjectively and objectively.Results: Our results show that automation failure negatively affects the human’s trustworthiness, as well as their trust in and liking of the automation.Discussion: Learning the effects of automation failure in trust and trustworthiness can contribute to a better understanding of the nature and dynamics of trust in these teams and improving human-automation teamwork.

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“…The literature has burgeoned from a seminal review article by Lee and See (2004), which outlines the trust process in human-automation interactions. Lee and See’s theoretical lens has been used to investigate trust and trustworthiness in a variety of subfields in addition to trust in automation (Hoff & Bashir, 2015), such as human-robot (Alarcon et al, 2023) and human-cognitive agent interaction (de Visser et al, 2016). A key aspect of Lee and See’s (2004) model focuses on defining automation trustworthiness, which comprises perceptions of automation performance, purpose, and process.…”

Section: Introductionmentioning

confidence: 99%

“…Whereas Lee and See (2004) discuss perceptions of automation trustworthiness, we refer to perceptions of systems, as their theory has been applied more broadly across subfields. Nearly two decades of research has applied this model to investigate human trustworthiness perceptions of automation (Chancey et al, 2017), robot (Alarcon et al, 2023), and robotic swarm referents (Hamdan et al, 2021). Researchers face several unique challenges when creating a scale to assess trustworthiness perceptions toward systems.…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of the System Trustworthiness Scale

et al. 2023

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Objective We created and validated a scale to measure perceptions of system trustworthiness. Background Several scales exist in the literature that attempt to assess trustworthiness of system referents. However, existing measures suffer from limitations in their development and validation. The current study sought to develop a scale based on theory and methodological rigor. Method We conducted exploratory and confirmatory factor analyses on data from two online studies to develop the System Trustworthiness Scale (STS). Additional analyses explored the manipulation of the factors and assessed convergent and divergent validity. Results The exploratory factor analyses resulted in a three-factor solution that represented the theoretical constructs of trustworthiness: performance, purpose, and process. Confirmatory factor analyses confirmed the three-factor solution. In addition, correlation and regression analyses demonstrated the scale’s divergent and predictive validity. Conclusion The STS is a psychometrically valid and predictive scale for assessing trustworthiness perceptions of system referents. Applications The STS assesses trustworthiness perceptions of systems. Importantly, the scale differentiates performance, purpose, and process constructs and is adaptable to a variety of system referents.

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Differential biases in human-human versus human-robot interactions

Cited by 15 publications

References 27 publications

Introduction to the Minitrack on Advances in Trust Research: How Context and Digital Technologies Matter

Introduction to the Minitrack on Advances in Trust Research: How Context and Digital Technologies Matter

Exploring the effect of automation failure on the human’s trustworthiness in human-agent teamwork

Development and Validation of the System Trustworthiness Scale

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