.png){kind=icon}
.png){kind=icon}
Return
Beyond Mere Algorithm Aversion: Are Judgments About Computer Agents More Variable?
.png){kind=icon}
PRE
AI
DOI：10.1177/00936502241303588.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
algorithm aversion
algorithm benefit
referential communication
AI source
human source
Journal
IF:
3.2
Papers:
146
Citations:
6.0K