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.

中文翻译：

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超越单纯的算法厌恶：对计算机代理的判断是否更加可变？


几项研究报告了算法厌恶，这反映在与犯相同错误的人类相比，对犯错的计算机的判断更严厉。两项在线研究 （ N = 67， N = 252） 测试了参考际任务是否可以获得类似的效果。参与者的任务是根据据称来自人类或 AI 来源的书面描述来识别日本汉字字符。至关重要的是，描述要么有缺陷（模棱两可），要么没有。捕获了实验试验期间的并发测量和有关来源的后问卷调查数据。研究 1 揭示了算法厌恶的模式，但也指出了 “算法优势 ”的相反效果：人工智能（与人类相比）的模棱两可的描述受到更负面的评价，但非模棱两可的描述受到更积极的评价，这表明对人工智能来源的判断可能表现出更大的可变性。研究 2 测试了这一预测。虽然人类和 AI 来源在并发测量方面没有差异，但问卷数据揭示了与可变性解释一致的几种模式。