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Unconscious Other’s Impression Changer: A Method to Manipulate Cognitive Biases That Subtly Change Others’ Impressions Positively/Negatively by Making AI Bias in Emotion Estimation AI

估计 认知 心理学 潜意识 现象 认知心理学 社会心理学 精神分析 量子力学 物理 经济 神经科学 管理
作者
Kyosuke Futami,Sadahiro Yanase,Kazuya Murao,Tsutomu Terada
出处
期刊:Sensors [Multidisciplinary Digital Publishing Institute]
卷期号:22 (24): 9961-9961 被引量:1
标识
DOI:10.3390/s22249961
摘要

Artificial Intelligence (AI) for human emotion estimation, such as facial emotion estimation, has been actively studied. On the other hand, there has been little research on unconscious phenomena in cognition and psychology (i.e., cognitive biases) caused by viewing AI emotion estimation information. Therefore, this study verifies RQ "Do people have a cognitive bias in which impressions of others (i.e., how to see and feel about others) are changed by viewing biased AI's emotion estimation information? If it exists, can impression manipulation methods that intentionally use this cognitive bias be realized?" The proposed method for verification makes the emotion estimation system biased so as to estimate emotion more positively/negatively than AI without bias. A prototype system was implemented. Evaluation using video showed that the presentation of biased emotion estimation information causes a phenomenon that quickly and unconsciously changes the way people see and feel others' impressions, which supported the RQ. Specifically, viewing information that estimated others' emotions more positively/negatively caused the phenomenon in which the user's self-judgment was overridden and others' impressions of emotions, words, and actions were perceived more positively/negatively. The existence of this phenomenon and method indicates that biased emotion estimation AI has the potential to both cause adverse effects on people and support people for good purposes through the manipulation of their impressions. This study provides helpful insights for the design and use of emotion estimation AI considering cognitive biases.

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