Predictive model to assess user trust

Ajenaghughrure, Ighoyota Ben; Sousa, Sónia; Kosunen, Ilkka; Lamas, David

doi:10.1145/3364183.3364195

Cited by 23 publications

(18 citation statements)

References 42 publications

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“…50% of these definitions are adopted directly from the social sciences literature on Human-Human trust [18,138,242] or adapted to Human-Machine trust, based on the grounds of social sciences [51,115,116,130] 6 . Other papers provide definitions based on a review of existing definitions of trust in Human-Machine trust [179] or propose their own based on Human-Machine and Human-Human trust definitions [2]. Finally, three definitions' origins were not provided 1.…”

Section: Definitions In Human-ai Trustmentioning

confidence: 99%

“…Another was is Electroencephalography (EEG) (𝑛 = 2, 3%), which records activity of the brain. This approach is more promising as there is some evidence that the predominant brain areas correlated with trust are the frontal and occipital ones [226], but the papers in our corpus either did not deeply explore the EEG data [77] or used it primarily for a preliminary model construction [2]. Finally, hand trajectories [59], easily captured with a computer mouse has recently been shown to reflect the evolution of decision making as well as hesitations [59,132].…”

Section: Physiological Measuresmentioning

confidence: 99%

“…Thus, designing and ensuring trustworthy AI has raised interest in HCI. For instance, previous work has looked at what factors influence users' trust and how [26,183,248], how trust is established and developed [3,167,245], and how it can be modeled [2,105]. However, trust remains a highly challenging theoretical concept to study due to its multidisciplinary and multifaceted nature [119,129].…”

Section: Introductionmentioning

confidence: 99%

“…All the numbers reported are as of beginning of January 2021 4. We found several keywords including automated decision aid (e.g.,[91]), AI-based decision support system (e.g.,[24]), intelligent assistant (e.g.,[2]), intelligent agent (e.g.,[214]), classifier (e.g.,[237]), etc 5. Henceforth, all reported percentages are rounded up to the nearest tenth.Proc.…”

mentioning

confidence: 99%

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How to Evaluate Trust in AI-Assisted Decision Making? A Survey of Empirical Methodologies

Vereschak

Bailly

Caramiaux

2021

Proc. ACM Hum.-Comput. Interact.

104

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The spread of AI-embedded systems involved in human decision making makes studying human trust in these systems critical. However, empirically investigating trust is challenging. One reason is the lack of standard protocols to design trust experiments. In this paper, we present a survey of existing methods to empirically investigate trust in AI-assisted decision making and analyse the corpus along the constitutive elements of an experimental protocol. We find that the definition of trust is not commonly integrated in experimental protocols, which can lead to findings that are overclaimed or are hard to interpret and compare across studies. Drawing from empirical practices in social and cognitive studies on human-human trust, we provide practical guidelines to improve the methodology of studying Human-AI trust in decision-making contexts. In addition, we bring forward research opportunities of two types: one focusing on further investigation regarding trust methodologies and the other on factors that impact Human-AI trust.CCS Concepts: • Human-centered computing → HCI theory, concepts and models.

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Section: Definitions In Human-ai Trustmentioning

confidence: 99%

Section: Physiological Measuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

How to Evaluate Trust in AI-Assisted Decision Making? A Survey of Empirical Methodologies

Vereschak

Bailly

Caramiaux

2021

Proc. ACM Hum.-Comput. Interact.

104

View full text Add to dashboard Cite

show abstract

“…More recently, there has been a trend to move away from self-report measures towards more "objective" measures of trust [16,17,[86][87][88]. These include physiological measures such as eye-tracking [89,90], social-cues extracted from video feed/cameras [91,92], audio [93][94][95], skin response [96,97], and neural measures [96][97][98][99], as well as play behavior in behavioral economic games [37,92,100].…”

Section: Challenges and Opportunities For Trust Researchmentioning

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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Modeling Trust in Human-Robot Interaction: A Survey

Khavas

Ahmadzadeh

Robinette

2020

Lecture Notes in Computer Science

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As the autonomy and capabilities of robotic systems increase, they are expected to play the role of teammates rather than tools and interact with human collaborators in a more realistic manner, creating a more human-like relationship. Given the impact of trust observed in human-robot interaction (HRI), appropriate trust in robotic collaborators is one of the leading factors influencing the performance of human-robot interaction. Team performance can be diminished if people do not trust robots appropriately by disusing or misusing them based on limited experience. Therefore, trust in HRI needs to be calibrated properly, rather than maximized, to let the formation of an appropriate level of trust in human collaborators. For trust calibration in HRI, trust needs to be modeled first. There are many reviews on factors affecting trust in HRI [22], however, as there are no reviews concentrated on different trust models, in this paper, we review different techniques and methods for trust modeling in HRI. We also present a list of potential directions for further research and some challenges that need to be addressed in future work on human-robot trust modeling.

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Predictive model to assess user trust

Cited by 23 publications

References 42 publications

How to Evaluate Trust in AI-Assisted Decision Making? A Survey of Empirical Methodologies

How to Evaluate Trust in AI-Assisted Decision Making? A Survey of Empirical Methodologies

Trust in Robots: Challenges and Opportunities

Modeling Trust in Human-Robot Interaction: A Survey

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