Do You Trust Me, Blindly? Factors Influencing Trust Towards a Robot Recommender System

Herse, Sarita; Vitale, Jonathan; Tonkin, Meg; Ebrahimian, Daniel; Ojha, Suman; Johnston, Benjamin; Judge, William Q.; Williams, Mary-Anne

doi:10.1109/roman.2018.8525581

Cited by 50 publications

(15 citation statements)

References 32 publications

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“…Furthermore, automation bias can foster the process of "deskilling", either because of the attrition of existing skills or due to the lack of skill development in general (Arnold & Sutton, 1998;Sutton et al, 2018). Such pro-blems highlight the overall risk of inappropriate trust of humans toward AI (Herse et al, 2018).…”

Section: Risks and Dark Sides Of Ai Usagementioning

confidence: 99%

Explainable Artificial Intelligence: Objectives, Stakeholders, and Future Research Opportunities

Meske

Bunde

Schneider

et al. 2020

Information Systems Management

225

132

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Artificial Intelligence (AI) has diffused into many areas of our private and professional life. In this research note, we describe exemplary risks of black-box AI, the consequent need for explainability, and previous research on Explainable AI (XAI) in information systems research. Moreover, we discuss the origin of the term XAI, generalized XAI objectives, and stakeholder groups, as well as quality criteria of personalized explanations. We conclude with an outlook to future research on XAI.

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Section: Risks and Dark Sides Of Ai Usagementioning

confidence: 99%

Explainable Artificial Intelligence: Objectives, Stakeholders, and Future Research Opportunities

Meske

Bunde

Schneider

et al. 2020

Information Systems Management

225

132

View full text Add to dashboard Cite

show abstract

“…To date, no systematic review has investigated and synthesised the current evidence on people's attitudes toward, trust in, anxiety associated with, and acceptance of social robots. Evidence suggest that all of these beliefs can predict the use of social robots [15,16], and reflect the same broad construct [17][18][19][20], which is people's perception or evaluation of robots.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review of Attitudes, Anxiety, Acceptance, and Trust Towards Social Robots

Naneva

Gou

Webb

et al. 2020

Int J of Soc Robotics

224

116

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As social robots become more common, there is a need to understand how people perceive and interact with such technology. This systematic review seeks to estimate people's attitudes toward, trust in, anxiety associated with, and acceptance of social robots; as well as factors that are associated with these beliefs. Ninety-seven studies were identified with a combined sample of over 13,000 participants and a standardized score was computed for each in order to represent the valence (positive, negative, or neutral) and magnitude (on a scale from 1 to − 1) of people's beliefs about robots. Potential moderating factors such as the robots' domain of application and design, the type of exposure to the robot, and the characteristics of potential users were also investigated. The findings suggest that people generally have positive attitudes towards social robots and are willing to interact with them. This finding may challenge some of the existing doubt surrounding the adoption of robotics in social domains of application but more research is needed to fully understand the factors that influence attitudes. Keywords Attitudes toward technology • Social robots • Systematic review • Human-robot interaction Stanislava Naneva and Marina Sarda Gou have contributed equally to this work.

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“…An expert group on Artificial Intelligence (AI) appointed by the European Commission states: "Without AI systems [..] being demonstrably worthy of trust, unwanted consequences may ensue and their uptake might be hindered" [1, p. 4]. Many AI systems are "black boxes" in that the reasons for their decisions and recommendations remain hidden from their users [2]. Consequently, users blindly follow AI systems' recommendations, distrust their decisions, or do not use the systems at all [2,3].…”

Section: Introductionmentioning

confidence: 99%

“…Many AI systems are "black boxes" in that the reasons for their decisions and recommendations remain hidden from their users [2]. Consequently, users blindly follow AI systems' recommendations, distrust their decisions, or do not use the systems at all [2,3].…”

Section: Introductionmentioning

confidence: 99%

Capturing Users’ Reality: A Novel Approach to Generate Coherent Counterfactual Explanations

Förster

Hühn²,

Klier

et al. 2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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The opacity of Artificial Intelligence (AI) systems is a major impediment to their deployment. Explainable AI (XAI) methods that automatically generate counterfactual explanations for AI decisions can increase users' trust in AI systems. Coherence is an essential property of explanations but is not yet addressed sufficiently by existing XAI methods. We design a novel optimizationbased approach to generate coherent counterfactual explanations, which is applicable to numerical, categorical, and mixed data. We demonstrate the approach in a realistic setting and assess its efficacy in a humangrounded evaluation. Results suggest that our approach produces explanations that are perceived as coherent as well as suitable to explain the factual situation.

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Do You Trust Me, Blindly? Factors Influencing Trust Towards a Robot Recommender System

Cited by 50 publications

References 32 publications

Explainable Artificial Intelligence: Objectives, Stakeholders, and Future Research Opportunities

Explainable Artificial Intelligence: Objectives, Stakeholders, and Future Research Opportunities

A Systematic Review of Attitudes, Anxiety, Acceptance, and Trust Towards Social Robots

Capturing Users’ Reality: A Novel Approach to Generate Coherent Counterfactual Explanations

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