Jürgen Buder, Fritz Becker, Janika Bareiß, Markus Huff
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引用次数: 0
Abstract
Several studies have reported algorithm aversion, reflected in harsher judgments about computers that commit errors, compared to humans who commit the same errors. Two online studies ( N = 67, N = 252) tested whether similar effects can be obtained with a referential communication task. Participants were tasked with identifying Japanese kanji characters based on written descriptions allegedly coming from a human or an AI source. Crucially, descriptions were either flawed (ambiguous) or not. Both concurrent measures during experimental trials and pre-post questionnaire data about the source were captured. Study 1 revealed patterns of algorithm aversion but also pointed at an opposite effect of “algorithm benefit”: ambiguous descriptions by an AI (vs. human) were evaluated more negatively, but non-ambiguous descriptions were evaluated more positively, suggesting the possibility that judgments about AI sources exhibit larger variability. Study 2 tested this prediction. While human and AI sources did not differ regarding concurrent measures, questionnaire data revealed several patterns that are consistent with the variability explanation.
期刊介绍:
Empirical research in communication began in the 20th century, and there are more researchers pursuing answers to communication questions today than at any other time. The editorial goal of Communication Research is to offer a special opportunity for reflection and change in the new millennium. To qualify for publication, research should, first, be explicitly tied to some form of communication; second, be theoretically driven with results that inform theory; third, use the most rigorous empirical methods; and fourth, be directly linked to the most important problems and issues facing humankind. Critieria do not privilege any particular context; indeed, we believe that the key problems facing humankind occur in close relationships, groups, organiations, and cultures.