Richard Pak

Professor

Clemson University

United States

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Dr. Richard Pak is a Professor at Clemson University in the United States. His research focuses on the interactions between trust, ethics, and performance in autonomous and human-AI systems, as evidenced by his recent articles. His work investigates the psychological aspects of trust repair strategies and the role of attention control in human performance within mixed-automation environments.

Articles (14)

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2024

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2024

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2023

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2023

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2023

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2023

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2023

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2022

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2022

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2022

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2022

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2021

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2020

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2020

Collaborators (5)

Christopher Flathmann

Research Assistant Professor

Clemson University

UNITED STATES

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Randall Engle

Prof

Georgia Institute of Technology

UNITED STATES

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Christopher Draheim

Visiting Assistant Professor

Lawrence University

UNITED STATES

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Moses Namara

UNITED STATES

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Mark Neerincx

Full Professor

Delft University of Technology

NETHERLANDS

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Richard Pak

Research Interests

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About

Articles (14)

A theoretical model to explain mixed effects of trust repair strategies in autonomous systems

Knowledge, attention, and psychomotor ability: A latent variable approach to understanding individual differences in simulated work performance

The Relevance of Attention Control, Not Working Memory, in Human Factors

Enhancing component-specific trust with consumer automated systems through humanness design

A theoretical model to explain mixed effects of trust repair strategies in autonomous systems

Nature and measurement of attention control.

The complex relationship of AI ethics and trust in human–AI teaming: insights from advanced real-world subject matter experts

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

Investigating the Effects of Perceived Teammate Artificiality on Human Performance and Cognition

The role of attention control in complex real-world tasks

Towards Ethical AI: Empirically Investigating Dimensions of AI Ethics, Trust Repair, and Performance in Human-AI Teaming

Near-Perfect Automation: Investigating Performance, Trust, and Visual Attention Allocation

Factors that affect younger and older adults’ causal attributions of robot behaviour

Towards a Theory of Longitudinal Trust Calibration in Human–Robot Teams

Collaborators (5)

Christopher Flathmann

Randall Engle

Christopher Draheim

Moses Namara

Mark Neerincx

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