The reliability and transparency bases of trust in human-swarm interaction: principles and implications

Hussein, Aya; Elsawah, Sondoss; Abbass, Hussein A.

doi:10.1080/00140139.2020.1764112

Cited by 26 publications

(26 citation statements)

References 52 publications

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“…To summarize, our findings regarding accumulation rates represent a significant theoretical advance in explaining benchmark effects in the HAT literature regarding increased automation reliance with increased reliability (e.g., Hussein et al, 2020a; Wiegmann et al, 2001), and more specifically, increased automation reliance when decision aids are more reliable relative to human manual ability (e.g., Avril et al, 2021; Bailey & Scerbo, 2007; Hutchinson et al, 2022). Our computational model suggested that two processes drove the effects of automation reliability.…”

Section: Discussionmentioning

confidence: 63%

“…Automation reliability (accuracy) is a crucial factor in determining human reliance on automation and subsequent human–automation teaming 1 (HAT) performance outcomes, both when human operators passively supervise automation that is performing tasks for them (e.g., Ferraro et al, 2018), and when operators perform tasks themselves while provided with decision aids that advise actions (e.g., Hussein et al, 2020a; Rovira et al, 2007; Shah & Bliss, 2017; Wiegmann et al, 2001). On the one hand, low-reliability automation is often undesirable because it can cause human operators to drop below manual (no automation) levels of accuracy (Wickens & Dixon, 2007).…”

Section: An Evidence Accumulation Model Of Automation Usementioning

confidence: 99%

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Automated decision aids: When are they advisors and when do they take control of human decision making?

Strickland¹,

Boag²,

Heathcote³

et al. 2023

Journal of Experimental Psychology: Applied

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We applied a computational model to examine the extent to which participants used an automated decision aid as an advisor, as compared to a more autonomous trigger of responding, at varying levels of decision aid reliability. In an air traffic control conflict detection task, we found higher accuracy when the decision aid was correct, and more errors when the decision aid was incorrect, as compared to a manual condition (no decision aid). Responses that were correct despite incorrect automated advice were slower than matched manual responses. Decision aids set at lower reliability (75%) had smaller effects on choices and response times, and were subjectively trusted less, than decision aids set at higher reliability (95%). We fitted an evidence accumulation model to choices and response times to measure how information processing was affected by decision aid inputs. Participants primarily treated low-reliability decision aids as an advisor rather than directly accumulating evidence based on its advice. Participants directly accumulated evidence based upon the advice of high-reliability decision aids, consistent with granting decision aids more autonomous influence over decisions. Individual differences in the level of direct accumulation correlated with subjective trust, suggesting a cognitive mechanism by which trust impacts human decisions.

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Section: Discussionmentioning

confidence: 63%

Section: An Evidence Accumulation Model Of Automation Usementioning

confidence: 99%

Automated decision aids: When are they advisors and when do they take control of human decision making?

Strickland¹,

Boag²,

Heathcote³

et al. 2023

Journal of Experimental Psychology: Applied

View full text Add to dashboard Cite

show abstract

“…Riley et al (2010) argued that low-level information negatively impacts operators’ cognitive load, as they must process it to build higher levels of SA ( Riley and Strater, 2006 ). In contrast, swarm-level information enables the human to make sense of swarm behaviors leading to effective collaboration ( Hussein et al, 2020a ). Finally, communication issues impose limits on the amount and speed of information exchange between the human and the swarm.…”

Section: Mission Complexitymentioning

confidence: 99%

Characterization of Indicators for Adaptive Human-Swarm Teaming

et al. 2022

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Swarm systems consist of large numbers of agents that collaborate autonomously. With an appropriate level of human control, swarm systems could be applied in a variety of contexts ranging from urban search and rescue situations to cyber defence. However, the successful deployment of the swarm in such applications is conditioned by the effective coupling between human and swarm. While adaptive autonomy promises to provide enhanced performance in human-machine interaction, distinct factors must be considered for its implementation within human-swarm interaction. This paper reviews the multidisciplinary literature on different aspects contributing to the facilitation of adaptive autonomy in human-swarm interaction. Specifically, five aspects that are necessary for an adaptive agent to operate properly are considered and discussed, including mission objectives, interaction, mission complexity, automation levels, and human states. We distill the corresponding indicators in each of the five aspects, and propose a framework, named MICAH (i.e., Mission-Interaction-Complexity-Automation-Human), which maps the primitive state indicators needed for adaptive human-swarm teaming.

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“…This element can have substantive impact on the user's trust; at the physical level, [47] found that a robot that could convey its incapabilities solely via informative arm movements was found to be more trustworthy by users. Similarly, robots that came across as transparent, either by providing explanations for its actions [48,49] or simply by providing more information about its actions [50,51], were judged as more trustworthy. At a more cognitive level, one study suggests that robots that took risky actions in an uncertain environment were viewed with distrust [52], although this depends on the individual user's risk appetite [53].…”

Section: Exploiting the Processmentioning

confidence: 99%

Trust in Robots: Challenges and Opportunities

2020

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Purpose of Review To assess the state-of-the-art in research on trust in robots and to examine if recent methodological advances can aid in the development of trustworthy robots. Recent Findings While traditional work in trustworthy robotics has focused on studying the antecedents and consequences of trust in robots, recent work has gravitated towards the development of strategies for robots to actively gain, calibrate, and maintain the human user's trust. Among these works, there is emphasis on endowing robotic agents with reasoning capabilities (e.g., via probabilistic modeling). Summary The state-of-the-art in trust research provides roboticists with a large trove of tools to develop trustworthy robots. However, challenges remain when it comes to trust in real-world human-robot interaction (HRI) settings: there exist outstanding issues in trust measurement, guarantees on robot behavior (e.g., with respect to user privacy), and handling rich multidimensional data. We examine how recent advances in psychometrics, trustworthy systems, robot-ethics, and deep learning can provide resolution to each of these issues. In conclusion, we are of the opinion that these methodological advances could pave the way for the creation of truly autonomous, trustworthy social robots.

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The reliability and transparency bases of trust in human-swarm interaction: principles and implications

Cited by 26 publications

References 52 publications

Automated decision aids: When are they advisors and when do they take control of human decision making?

Automated decision aids: When are they advisors and when do they take control of human decision making?

Characterization of Indicators for Adaptive Human-Swarm Teaming

Trust in Robots: Challenges and Opportunities

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