1. Resisting Artificial Intelligence: When Do Decision-Makers Avoid or Use Algorithmic Input?
- Author
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Hee Jin Yang, Heather, Fast, Nathanael, Hildebrand, Christian, Hoffman, Donna, Logg, Jennifer Marie, and Yeomans, Michael
- Abstract
When do people heed the advice from an algorithm as opposed to a human? This session delves into novel research in which people opt for the recommendations from algorithmically-derived sources and uses experimental approaches to quantify and theoretically inform the conditions in which decision-makers listen to (or ignore) algorithmic advice. The papers in this session examine a breadth of decision-making scenarios and a diverse range of contextual factors, including: contrast in stated preferences and behavioral decisions; domain sensitivity of algorithmic advice; conversational fluency in evaluations of AI competence; gender-status stereotype congruence of AI agents; and the effect of anthropomorphization of algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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