The influence of agent reliability on trust in human-agent collaboration

Fan, Xin; Oh, Soo‐Young; McNeese, Michael D.; Yen, John; Cuevas, Haydee M.; Strater, Laura D.; Endsley, Mica R.

doi:10.1145/1473018.1473028

Cited by 55 publications

(41 citation statements)

References 11 publications

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“…However, a third theme was that transparency appeared to play an interactive role in the effects of the autonomous agent's reliability. Specifically, when human operators were aware of the lower reliability (higher transparency), they were more trusting (Fan et al, 2008) and their performance was higher than when they were unaware of the reliability (Chen & Barnes, 2012).…”

Section: Independent Variablesmentioning

confidence: 99%

Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature

et al. 2020

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Objective We define human–autonomy teaming and offer a synthesis of the existing empirical research on the topic. Specifically, we identify the research environments, dependent variables, themes representing the key findings, and critical future research directions. Background Whereas a burgeoning literature on high-performance teamwork identifies the factors critical to success, much less is known about how human–autonomy teams (HATs) achieve success. Human–autonomy teamwork involves humans working interdependently toward a common goal along with autonomous agents. Autonomous agents involve a degree of self-government and self-directed behavior (agency), and autonomous agents take on a unique role or set of tasks and work interdependently with human team members to achieve a shared objective. Method We searched the literature on human–autonomy teaming. To meet our criteria for inclusion, the paper needed to involve empirical research and meet our definition of human–autonomy teaming. We found 76 articles that met our criteria for inclusion. Results We report on research environments and we find that the key independent variables involve autonomous agent characteristics, team composition, task characteristics, human individual differences, training, and communication. We identify themes for each of these and discuss the future research needs. Conclusion There are areas where research findings are clear and consistent, but there are many opportunities for future research. Particularly important will be research that identifies mechanisms linking team input to team output variables.

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Section: Independent Variablesmentioning

confidence: 99%

Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature

et al. 2020

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“…Fan et al [14] tested different levels of agents' variability (using systematic biases) in a turn-based C2 threat assessment task. They found that informing participants of the agent's errors helps users calibrate their trust accordingly, which leads to higher task performance.…”

Section: Performance and Reliabilitymentioning

confidence: 99%

Impact of Agent Reliability and Predictability on Trust in Real Time Human-Agent Collaboration

Daronnat

Azzopardi

Halvey

et al. 2020

Proceedings of the 8th International Conference on Human-Agent Interaction

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Trust is a prerequisite for effective human-agent collaboration. While past work has studied how trust relates to an agent's reliability, it has been mainly carried out in turn based scenarios, rather than during real-time ones. Previous research identified the performance of an agent as a key factor influencing trust. In this work, we posit that an agent's predictability also plays an important role in the trust relationship, which may be observed based on users' interactions. We designed a 2x2 within-groups experiment with two baseline conditions: (1) no agent (users' individual performance), and (2) near-flawless agent (upper bound). Participants took part in an interactive aiming task where they had to collaborate with different agents that varied in terms of their predictability, and were controlled in terms of their performance. Our results show that agents whose behaviours are easier to predict have a more positive impact on task performance, reliance and trust while reducing cognitive workload. In addition, we modelled the human-agent trust relationship and demonstrated that it is possible to reliably predict users' trust ratings using real-time interaction data. This work seeks to pave the way for the development of trust-aware agents capable of adapting and responding more appropriately to users. CCS CONCEPTS • Human-centered computing → Empirical studies in HCI; Collaborative interaction; User studies; Laboratory experiments.

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“…People with higher expertise in a domain have been found to rely less on automation and detect automation failures more readily (de Boer, Heems, & Hurts, ; Fan et al., ; Sanchez et al., ). For instance, experienced pilots detected an unexpected automation failure more quickly and more reliably than pilots who had just finished flight school (de Boer et al., ).…”

Section: Discussionmentioning

confidence: 99%

“…People with higher expertise in a domain have been found to rely less on automation and detect automation failures more readily (de Boer, Heems, & Hurts, 2014;Fan et al, 2008;Sanchez et al, 2014).…”

Section: Job Experiencementioning

confidence: 99%

Case‐based decision support for fault diagnosis: Do salient ratings discourage situation analysis?

Müller

Ullrich

Dessel

et al. 2018

Hum Ftrs & Erg Mfg Svc

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In the processing industry, frequent faults call for assistance in diagnosis, and case‐based reasoning (CBR) can provide solutions applied by other operators in the past. This study investigated whether salient case ratings promote an uncritical acceptance of solutions. In 2 experiments, subjects diagnosed faults with a simulated CBR system, and ratings were presented in graphical or verbal format. In most trials, the case with the highest rating provided the correct solution, while in catch‐trials, it did not. Graphical ratings were hypothesized to speed up solutions but discourage cross‐checking and lead to errors in catch‐trials. These hypotheses were not confirmed, even though Experiment 2 maximized the incentive of relying on case ratings. While graphical ratings led subjects to start with the most highly rated case, they did not impair situation analysis and accuracy. The results suggest that during fault diagnosis people are not easily misled into overtrusting a CBR system.

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The influence of agent reliability on trust in human-agent collaboration

Cited by 55 publications

References 11 publications

Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature

Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature

Impact of Agent Reliability and Predictability on Trust in Real Time Human-Agent Collaboration

Case‐based decision support for fault diagnosis: Do salient ratings discourage situation analysis?

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