Having the Right Attitude: How Attitude Impacts Trust Repair in Human—Robot Interaction

Abstract

Robot co-workers, like human co-workers, make mistakes that undermine trust. Yet, trust is just as important in promoting human-robot collaboration as it is in promoting human-human collaboration. In addition, individuals can significantly differ in their attitudes toward robots, which can also impact or hinder their trust in robots. To better understand how individual attitude can influence trust repair strategies, we propose a theoretical model that draws from the theory of cognitive dissonance. To empirically verify this model, we conducted a between-subjects experiment with 100 participants assigned to one of four repair strategies (apologies, denials, explanations, or promises) over three trust violations. Individual attitudes did moderate the efficacy of repair strategies and this effect differed over successive trust violations. Specifically, repair strategies were most effective relative to individual attitude during the second of the three trust violations, and promises were the trust repair strategy most impacted by an individual's attitude.

Measuring and Understanding Trust Calibrations for Automated Systems: A Survey of the State-Of-The-Art and Future Directions

“…To that end, Kohn and colleagues [64] found that apologies worked better to regain trust than a system denying an error. However, Esterwood and Robert [34] could show that promising no errors in the future as well as explanations for why the error happened showed greater effects than mere apologies (see also results by [136]). In contrast to that, Robinette et al [112] and Schelble et al [116] found no effects of apologies on trust repair.…”

“…Screening of dangerous objects/subjects [4, 14, 15, 19, 36, 51, 54-56, 85, 94, 107, 117], detection of system malfunctions [89,90], crime prevention [10], recidivism prediction [132], watch a video of a house search [66,83] Transportation Responding to take-over requests [2, 6, 7, 47, 61, 68-70, 77, 84, 98], collusion avoidance [8], managing (air) traffic [31,118], pedestrians interaction with AV [48], observe AVs [64,119], drive in driving simulator [87,96,136] Military Screening tasks [17,33,44,63,81,82,127,130,135,139,140], gathering of information [58], mission planning [92], human-AI collaboration for search and destroy missions [116] Production Improving production [73,133,143], disassembly [5], moving objects [34,35,45], demand forecasting [39], harvesting [113], quality checks [142] Gaming trust game [3,23], collaboration game [24], flanker task…”

Optimizing Voice Recognition Informatic Robots for Effective Communication in Outpatient Settings

“…Outpatient clinics consistently serve as crucial windows for patients seeking medical care. However, the introduction of informatic robots, while enhancing efficiency, potentially erodes the essential trust inherent in healthcare interactions [ 6 ]. Patients may understandably harbor reservations about entrusting their care trajectory to a mechanized entity, potentially leading to discomfort.…”

Exploring the Relationship Between Ethics and Trust in Human–Artificial Intelligence Teaming: A Mixed Methods Approach

“…It is possible that the participants did not value apologies and denials because it was not supported by behavioral change, an effect found in interpersonal trust literature (Schweitzer et al, 2006) and in the human-robot literature (Luo et al, 2021). Other trust repair strategies that could be more effective with machine advice include an expression of regret that accompanies the apology (Kox et al, 2021), delaying the repair strategy until the next trust opportunity (Nayyar & Wagner, 2018;Robinette et al, 2015), providing promises or explanations that reduce cognitive dissonance between initial attitudes and experiences (Esterwood & Robert, 2022), or adding human-like qualities to expression of the trust repair strategy (de Visser et al, 2016;Kim & Song, 2021).…”