Convergence Across Behavioral and Self-report Measures Evaluating Individuals' Trust in an Autonomous Golf Cart

Cotter, Jenna E.; O’Hear, Emily H.; Smitherman, R. Cooper; Bright, Addison B.; Tenhundfeld, Nathan L.; Forsyth, Jason; Sprague, Nathan; El-Tawab, Samy

doi:10.1109/sieds55548.2022.9799431

Cited by 5 publications

(4 citation statements)

References 39 publications

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“…Researchers of trust have looked at behavioral aspects of machines to elicit and measure trust. However, [25] found little convergence across trust measures within the empirical research. For instance, measuring trust has encompassed a broad range of techniques such as self-reported measures through surveys [26], [27], correlation with biofeedback such as galvanic skin response [28], eye gaze [29], [30], binary measures: use (trust)/disuse (distrust) [31]; behavioral measures [32], [33], [34].…”

Section: Trust In Ai For Decision-makingmentioning

confidence: 86%

“…The state |𝐴, 𝐷⟩ in equation ( 25) represents the state that the decision maker agrees with AI and will delegate the decision to the AI. Due to the nature of the quantum model, the initial probability distribution is in a superposition of all of the states shown in equation (25). Initial probability distribution of this model is represented by a 4 x 1 column vector:…”

Section: [ 𝑝 𝐴𝐷 (𝑡) 𝑝 𝐴𝑛𝑜𝑡𝐷 (𝑡) 𝑝 𝐷𝑖𝑠𝐴𝐷 (𝑡) 𝑝 𝐷𝑖𝑠𝐴𝑛𝑜𝑡𝐷 (𝑡)] (16)mentioning

confidence: 99%

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Intermediate Judgments and Trust in Artificial Intelligence-Supported Decision-Making

Humr,

Canan

2024

Preprint

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Human decision-making is increasingly supported by artificial intelligence (AI) systems. From medical imaging analysis to self-driving vehicles, AI systems are becoming organically embedded in a host of different technologies. However, incorporating such advice into decision-making entails a human rationalization of AI outputs for supporting beneficial outcomes. Recent research suggests intermediate judgments in the first stage of a decision process can interfere with decisions in subsequent stages. For this reason, we extend this research to AI-supported decision-making to investigate how intermediate judgments on AI-provided advice may influence subsequent decisions. In an online experiment (N=192), we found a consistent bolstering effect in trust for those who made intermediate judgments and over those who did not. Furthermore, violations of total probability were observed at all timing intervals throughout the study. We further analyzed the results by demonstrating how quantum probability theory can model these types of behaviors in human-AI decision-making and ameliorate the understanding the interaction dynamics at the confluence of human factors and information features.

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Section: Trust In Ai For Decision-makingmentioning

confidence: 86%

Section: [ 𝑝 𝐴𝐷 (𝑡) 𝑝 𝐴𝑛𝑜𝑡𝐷 (𝑡) 𝑝 𝐷𝑖𝑠𝐴𝐷 (𝑡) 𝑝 𝐷𝑖𝑠𝐴𝑛𝑜𝑡𝐷 (𝑡)] (16)mentioning

confidence: 99%

Intermediate Judgments and Trust in Artificial Intelligence-Supported Decision-Making

Humr,

Canan

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The state | A, D⟩ in Equation ( 25) represents the state where the decision-maker agrees with AI and will delegate the decision to the AI. Due to the nature of the quantum model, the initial probability distribution is in a superposition of all of the states shown in Equation (25). The initial probability distribution of this model is represented by a 4 × 1 column vector, as follows:…”

Section: Agree (A)mentioning

confidence: 99%

“…However, Ref. [ 25 ] found little convergence across trust measures within the empirical research. For instance, measuring trust has encompassed a broad range of techniques such as self-reported measures through surveys [ 26 , 27 ], correlation with biofeedback such as galvanic skin response [ 28 ], eye gaze [ 29 , 30 ], binary measures, such as use (trust)/disuse (distrust) [ 31 ], and behavioral measures [ 32 , 33 , 34 ].…”

Section: Introductionmentioning

confidence: 99%

Intermediate Judgments and Trust in Artificial Intelligence-Supported Decision-Making

Humr,

Canan

2024

Entropy

View full text Add to dashboard Cite

Human decision-making is increasingly supported by artificial intelligence (AI) systems. From medical imaging analysis to self-driving vehicles, AI systems are becoming organically embedded in a host of different technologies. However, incorporating such advice into decision-making entails a human rationalization of AI outputs for supporting beneficial outcomes. Recent research suggests intermediate judgments in the first stage of a decision process can interfere with decisions in subsequent stages. For this reason, we extend this research to AI-supported decision-making to investigate how intermediate judgments on AI-provided advice may influence subsequent decisions. In an online experiment (N = 192), we found a consistent bolstering effect in trust for those who made intermediate judgments and over those who did not. Furthermore, violations of total probability were observed at all timing intervals throughout the study. We further analyzed the results by demonstrating how quantum probability theory can model these types of behaviors in human–AI decision-making and ameliorate the understanding of the interaction dynamics at the confluence of human factors and information features.

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Responsible Integration of Autonomous Vehicles in an Autocentric Society

Bachmann

Conley

El-Tawab

et al. 2022

2022 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT)

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Convergence Across Behavioral and Self-report Measures Evaluating Individuals' Trust in an Autonomous Golf Cart

Cited by 5 publications

References 39 publications

Intermediate Judgments and Trust in Artificial Intelligence-Supported Decision-Making

Intermediate Judgments and Trust in Artificial Intelligence-Supported Decision-Making

Intermediate Judgments and Trust in Artificial Intelligence-Supported Decision-Making

Responsible Integration of Autonomous Vehicles in an Autocentric Society

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