Human Trust After Robot Mistakes: Study of the Effects of Different Forms of Robot Communication

Ye, Sean; Neville, Glen; Schrum, Mariah L.; Gombolay, Matthew; Chernova, Sonia; Howard, Ayanna M.

doi:10.1109/ro-man46459.2019.8956424

Cited by 19 publications

(7 citation statements)

References 11 publications

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“…Prior work suggests that people often discount advice of others relative to their own judgment if they have the option to choose (e.g., see Yaniv and Kleinberger 35 ). Therefore, only receiving AI advice upon request may help mitigate the over-reliance problem; further research should investigate the implications of presenting advice automatically versus upon request 36 .…”

Section: Discussionmentioning

confidence: 99%

Do as AI say: susceptibility in deployment of clinical decision-aids

et al. 2021

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Artificial intelligence (AI) models for decision support have been developed for clinical settings such as radiology, but little work evaluates the potential impact of such systems. In this study, physicians received chest X-rays and diagnostic advice, some of which was inaccurate, and were asked to evaluate advice quality and make diagnoses. All advice was generated by human experts, but some was labeled as coming from an AI system. As a group, radiologists rated advice as lower quality when it appeared to come from an AI system; physicians with less task-expertise did not. Diagnostic accuracy was significantly worse when participants received inaccurate advice, regardless of the purported source. This work raises important considerations for how advice, AI and non-AI, should be deployed in clinical environments.

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Section: Discussionmentioning

confidence: 99%

Do as AI say: susceptibility in deployment of clinical decision-aids

et al. 2021

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“…However, prior work has noted that stress, anxiety, and fear levels of a human user around a fast-moving robot with the capability of causing physical harm are high [3,6,11,50,60,62,65], presenting interesting challenges in producing high-performance HRC. Furthermore, as producing high-speed robotic maneuvers is a difficult challenge in itself, poor performance of the robot can adversely affect user trust [20,35,58,73]. As such, to address these open problems in studying agile cobots, we study both the effects of varying skill level and communication on collaboration performance between humans and agile robot.…”

Section: Related Workmentioning

confidence: 99%

The Effect of Robot Skill Level and Communication in Rapid, Proximate Human-Robot Collaboration

Lee

Krishna

Zaidi

et al. 2023

Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction

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As high-speed, agile robots become more commonplace, these robots will have the potential to better aid and collaborate with humans. However, due to the increased agility and functionality of these robots, close collaboration with humans can create safety concerns that alter team dynamics and degrade task performance. In this work, we aim to enable the deployment of safe and trustworthy agile robots that operate in proximity with humans. We do so by 1) Proposing a novel human-robot doubles table tennis scenario to serve as a testbed for studying agile, proximate human-robot collaboration and 2) Conducting a user-study to understand how attributes of the robot (e.g., robot competency or capacity to communicate) impact team dynamics, perceived safety, and perceived trust, and how these latent factors affect human-robot collaboration (HRC) performance. We find that robot competency significantly increases perceived trust (𝑝 < .001), extending skill-to-trust assessments in prior studies to agile, proximate HRC. Furthermore, interestingly, we find that when the robot vocalizes its intention to perform a task, it results in a significant decrease in team performance (𝑝 = .037) and perceived safety of the system (𝑝 = .009). CCS CONCEPTS• Human-centered computing → Empirical studies in collaborative and social computing; • Computer systems organization → Robotics.

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“…Trust is vital to promoting human and robot collaboration, but like human teammates robots make mistakes that undermine trust. As a result, a human's perception of a robot's trustworthiness can dramatically decrease [1], [2], [3], [13], [14], [15]. Trustworthiness is the degree to which a trustee is perceived as worthy of an individual's trust.…”

Section: Introductionmentioning

confidence: 99%

Do You Still Trust Me? Human-Robot Trust Repair Strategies

Esterwood

Robert

2021

2021 30th IEEE International Conference on Robot &Amp; Human Interactive Communication (RO-MAN)

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Trust is vital to promoting human and robot collaboration, but like human teammates, robots make mistakes that undermine trust. As a result, a human's perception of his or her robot teammate's trustworthiness can dramatically decrease [1], [2], [3], [4]. Trustworthiness consists of three distinct dimensions: ability (i.e. competency), benevolence (i.e. concern for the trustor) and integrity (i.e. honesty) [5], [6]. Taken together, decreases in trustworthiness decreases trust in the robot [7]. To address this, we conducted a 2 (high vs. low anthropomorphism) x 4 (trust repair strategies) between-subjects experiment. Preliminary results of the first 164 participants (between 19 and 24 per cell) highlight which repair strategies are effective relative to ability, integrity and benevolence and the robot's anthropomorphism. Overall, this paper contributes to the HRI trust repair literature.

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Human Trust After Robot Mistakes: Study of the Effects of Different Forms of Robot Communication

Cited by 19 publications

References 11 publications

Do as AI say: susceptibility in deployment of clinical decision-aids

Do as AI say: susceptibility in deployment of clinical decision-aids

The Effect of Robot Skill Level and Communication in Rapid, Proximate Human-Robot Collaboration

Do You Still Trust Me? Human-Robot Trust Repair Strategies

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