AI美容评估工具虽温和,仍加剧外貌焦虑与自我物化。
Psychological Effect of AI driven marketing tools for beauty/facial feature enhancement
- 对比两种版本的AI面部分析工具,考察其心理影响
- 无论评价方式强弱,高自我物化者自尊更低、更倾向修饰外貌
- 女性更易沉迷数字美颜,且低估他人情绪反应
AI驱动的面部评估工具正在重塑个体对外貌的认知及对社会评价的内化。本研究探讨此类工具对自我物化、自尊和情绪反应的心理影响,并关注性别差异。两组样本分别使用了明显批判性(N=75,M=22.9岁)和较中立性(N=51,M=19.9岁)的面部分析工具。参与者完成经验证的自我物化与自尊量表,以及自编的情绪、数字/物理外貌修饰(DAE, PAEE)和感知社交情绪(PSE)量表。结果表明,无论工具风格如何,高自我物化与低自尊均与更强的外貌修饰行为显著相关。尽管新工具表述更温和,但仍引发负面情绪(U=1466.5,p=0.013),说明隐性反馈可能强化外貌焦虑。性别差异在DAE(p=0.025)和PSE(p<0.001)中显著,女性更倾向于数字美化,且较少察觉他人情绪影响。研究揭示了AI工具可能无意中放大社会偏见,强调负责任设计的重要性。未来将探究训练数据中的意识形态如何塑造评估输出,并影响用户态度与决策。
原文摘要 · Abstract (English)
AI-powered facial assessment tools are reshaping how individuals evaluate appearance and internalize social judgments. This study examines the psychological impact of such tools on self-objectification, self-esteem, and emotional responses, with attention to gender differences. Two samples used distinct versions of a facial analysis tool: one overtly critical (N=75; M=22.9 years), and another more neutral (N=51; M=19.9 years). Participants completed validated self-objectification and self-esteem scales and custom items measuring emotion, digital/physical appearance enhancement (DAE, PAEE), and perceived social emotion (PSE). Results revealed consistent links between high self-objectification, low self-esteem, and increased appearance enhancement behaviors across both versions. Despite softer framing, the newer tool still evoked negative emotional responses (U=1466.5, p=0.013), indicating implicit feedback may reinforce appearance-related insecurities. Gender differences emerged in DAE (p=0.025) and PSE (p<0.001), with females more prone to digital enhancement and less likely to perceive emotional impact in others. These findings reveal how AI tools may unintentionally reinforce and amplify existing social biases and underscore the critical need for responsible AI design and development. Future research will investigate how human ideologies embedded in the training data of such tools shape their evaluative outputs, and how these, in turn, influence user attitudes and decisions.
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