More Is Not Always Better: Impacts of AI-Generated Confidence and Explanations in Human-Automation Interaction.

Journal: Human factors

PMID: 38437598

Abstract

OBJECTIVE: The study aimed to enhance transparency in autonomous systems by automatically generating and visualizing confidence and explanations and assessing their impacts on performance, trust, preference, and eye-tracking behaviors in human-automation interaction.

Authors

Shihong Ling

University of Pittsburgh, USA.
Yutong Zhang

Department of Biochemistry and Molecular Biology, Harbin Medical University, Harbin 150081, China.
Na Du

University of Pittsburgh, USA.

Keywords

Adult Artificial Intelligence Eye-Tracking Technology Female Humans Male Man-Machine Systems Task Performance and Analysis Trust Young Adult

External Resources

View on PubMed Access via DOI PubMed (38437598)

More Is Not Always Better: Impacts of AI-Generated Confidence and Explanations in Human-Automation Interaction.

Abstract

Authors

Keywords

External Resources

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