Neurological Measurement of Human Trust in Automation Using Electroencephalogram

Oh, Seeung; Seong, Younho; Sun, Yi; Park, Sangsung

doi:10.5391/ijfis.2020.20.4.261

Cited by 10 publications

(6 citation statements)

References 21 publications

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“…GSR was also shown to correlate with trust levels in studies of different contexts, such as the work of Montague et al ( 2014 ) and Khawaji et al ( 2015 ). Furthermore, there have been several recent studies examining EEG for detecting the neural markers of human trust, such as Wang et al ( 2018 ), Blais et al ( 2019 ), Jung et al ( 2019 ), and Oh et al ( 2020 ). Electrocardiography (ECG) is far less researched compared to EEG and GSR.…”

Section: Practical Frameworkmentioning

confidence: 99%

Empowering human-AI teams via Intentional Behavioral Synchrony

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Bhattacharya

2023

Front. Neuroergonomics

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As Artificial Intelligence (AI) proliferates across various sectors such as healthcare, transportation, energy, and military applications, the collaboration between human-AI teams is becoming increasingly critical. Understanding the interrelationships between system elements - humans and AI - is vital to achieving the best outcomes within individual team members' capabilities. This is also crucial in designing better AI algorithms and finding favored scenarios for joint AI-human missions that capitalize on the unique capabilities of both elements. In this conceptual study, we introduce Intentional Behavioral Synchrony (IBS) as a synchronization mechanism between humans and AI to set up a trusting relationship without compromising mission goals. IBS aims to create a sense of similarity between AI decisions and human expectations, drawing on psychological concepts that can be integrated into AI algorithms. We also discuss the potential of using multimodal fusion to set up a feedback loop between the two partners. Our aim with this work is to start a research trend centered on exploring innovative ways of deploying synchrony between teams of non-human members. Our goal is to foster a better sense of collaboration and trust between humans and AI, resulting in more effective joint missions.

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Section: Practical Frameworkmentioning

confidence: 99%

Empowering human-AI teams via Intentional Behavioral Synchrony

Naser

Bhattacharya

2023

Front. Neuroergonomics

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“…From different interpretations of trust in the context of human-machine teaming, it can be understood that a large component of human trust comes from an individual's experience. If humans experience poor performance from the machine, they will not trust the machine anymore (Oh et al 2020). Even trust can be changed in one single interaction (Hoff and Bashir 2015).…”

Section: Literature Reviewmentioning

confidence: 99%

A Preliminary Study on Human Trust in Pseudo-Real-Time Scenario through Electroencephalography and Machine Learning based Data Classification

Farzana Firoz,

Seong,

et al. 2023

Proceedings of the International Conference on Industrial Engineering and Operations Management

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This study aims to sense trust and distrust in a real-time inspired scenario through the classification of brain signals.Here, a word elicitation study is used to invoke the mental state associated with trust and distrust associated with the machine. Participants think of any event or experience that comes into their mind when they observe the word. They think or recall that event/experience without deliberately filtering out any kind of cognitive or affective mental state, which we consider as a replica of a real-life scenario where all kinds of mental states or emotions possibly co-exist along with trust or distrust. While thinking or recalling such events, Electroencephalography data is recorded from the participants' cortex and analyzed through Machine Learning approaches with several classification algorithms. The study developed an approach to sense whether the human is going through trust or distrust and compared different methods to discuss their efficacies in different scenarios. Here, the individualistic and generalistic approach is delved into, and it found that individualistic approaches provide better accuracy in sensing trust or distrust state of the human brain. Also, this study explored ways to increase the efficiency of the method by reducing the number of channels and compared the performance of the models by observing the loss of accuracy caused by the reduced number of channels. This study found that the K-Nearest Neighbor and/or Random Forest classifier algorithm provides the best result using raw data with the individualistic approach in most scenarios, achieving up to 100% average accuracy.

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“…More importantly, with respect to the brain regions that were activated during human-automation trust-related decision making, these recent EEG studies pinpointed anterior regions such as the (i) lateral prefrontal cortex [38], (ii) anterior cingulate cortex (ACC) [1], [3], and posterior regions such as the (iii) occipital cortex [38] and (iv) fusiform gyrus (also known as the occipitotemporal gyrus) [3]. Two studies further showed that power increases or variation in the beta frequency band (12 Hz -35 Hz) were associated with increased levels of trust [36] and making discriminatory judgments between trustworthy and untrustworthy stimuli [35]. Figure 2 shows the brain regions from which trust-related decision making EEG signals were recorded.…”

Section: A Eeg Studies 1) General Introductionmentioning

confidence: 99%

“…A phenomenon common to all these studies pertains to the use of experimental design or tasks that involved the modulation of reliability (or dependability) offered by an automated tool (Table II, column 2). Reliability refers to how consistent an automation can perform in providing accurate information and was largely manipulated through the programming and presentation of automated agents or advisories that can provide information with different probabilities of accuracy [35], [1], [36], [3] or risk-taking tendencies [37], [38].…”

Section: A Eeg Studies 1) General Introductionmentioning

confidence: 99%

Heading Toward Trusted ATCO-AI Systems: A Literature Review

Zhong¹

2021

Preprint

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The current review addresses emerging issues that arise from the creation of safe, beneficial, and trusted artificial intelligence–air traffic controller (AI-ATCO) systems for air traffic management (ATM). These issues concern trust between the human user and automated or AI tools of interest, resilience, safety, and transparency. To tackle these issues, we advocate the development of practical AI ATCO teaming frameworks by bringing together concepts and theories from neuroscience and explainable AI (XAI). By pooling together knowledge from both ATCO and AI perspectives, we seek to establish confidence in AI-enabled technologies for ATCOs. In this review, we present an overview of the extant studies that shed light on the research and development of trusted human-AI systems, and discuss the prospects of extending such works to building better trusted ATCO-AI systems. This paper contains three sections elucidating trust-related human performance, AI and explainable AI (XAI), and human-AI teaming.

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Neurological Measurement of Human Trust in Automation Using Electroencephalogram

Cited by 10 publications

References 21 publications

Empowering human-AI teams via Intentional Behavioral Synchrony

Empowering human-AI teams via Intentional Behavioral Synchrony

A Preliminary Study on Human Trust in Pseudo-Real-Time Scenario through Electroencephalography and Machine Learning based Data Classification

Heading Toward Trusted ATCO-AI Systems: A Literature Review

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