Dynamic modeling of trust in human-machine interactions

Akash, Kumar; Hu, Wan-Lin; Reid, Tahira; Jain, Neera

doi:10.23919/acc.2017.7963172

Cited by 43 publications

(19 citation statements)

References 20 publications

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“…On the other hand, the accuracy of the algorithm was switched between reliable and faulty according to a pseudo-random binary sequence (PRBS) in Database 3. This was done in order to excite all possible dynamics of the participant's trust response required for dynamic behavior modeling, which was the subject of related work by the authors [1]. Therefore, only the data from databases 1 and 2 (i.e., the first 40 trials) were analyzed.…”

Section: Methodsmentioning

confidence: 99%

“…We hypothesized that the trust level would be high in reliable trials and be low in faulty trials, and we validated this hypothesis using responses collected from 581 online participants (58 were outliers) via Amazon Mechanical Turk [2]. The experiment elicited expected trust responses based on the aggregated data as shown in Figure 7 [1]. Therefore, data from reliable trials were labeled as trust, and data from faulty trials were labeled as distrust.…”

Section: Methodsmentioning

confidence: 99%

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A Classification Model for Sensing Human Trust in Machines Using EEG and GSR

Akash

Jain

et al. 2018

ACM Trans. Interact. Intell. Syst.

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Today, intelligent machines interact and collaborate with humans in a way that demands a greater level of trust between human and machine. A first step towards building intelligent machines that are capable of building and maintaining trust with humans is the design of a sensor that will enable machines to estimate human trust level in real-time. In this paper, two approaches for developing classifier-based empirical trust sensor models are presented that specifically use electroencephalography (EEG) and galvanic skin response (GSR) measurements. Human subject data collected from 45 participants is used for feature extraction, feature selection, classifier training, and model validation. The first approach considers a general set of psychophysiological features across all participants as the input variables and trains a classifier-based model for each participant, resulting in a trust sensor model based on the general feature set (i.e., a "general trust sensor model"). The second approach considers a customized feature set for each individual and trains a classifier-based model using that feature set, resulting in improved mean accuracy but at the expense of an increase in training time. This work represents the first use of real-time psychophysiological measurements for the development of a human trust sensor. Implications of the work, in the context of trust management algorithm design for intelligent machines, are also discussed.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

A Classification Model for Sensing Human Trust in Machines Using EEG and GSR

Akash

Jain

et al. 2018

ACM Trans. Interact. Intell. Syst.

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“…A more general approach is to directly model the human's dynamic trust in the robot. Work in this area has focused on two problems: (i) estimating trust based on observations of the human's behavior [18,27,[57][58][59][60][61][62][63][64] and (ii) utilizing the estimate of trust to guide robot behavior [27,58,[65][66][67][68][69].…”

Section: Computational Trust Modelsmentioning

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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“…A large number of these models are qualitative models [15,30,40,43] which analyze the factors that affect trust but cannot be used to make quantitative predictions. Some quantitative models, including regression models [14,44] and time-series models of trust [2,27,29,[31][32][33]41], fill this gap but do not account for the probabilistic nature of human behavior.…”

Section: Related Workmentioning

confidence: 99%