This research examines whether competence cues can reduce gender bias in evaluations of AI managers and whether these effects depend on how the AI is represented. Across two preregistered experiments (N = 2,505), each employing a 2 x 2 x 3 design manipulating AI gender, competence, and decision outcome, we compared text-based descriptions of AI managers with visually generated AI faces created using a reverse-correlation paradigm. In the text condition, evaluations were driven by competence rather than gender. When participants received unfavourable decisions, high-competence AI managers were judged as fairer, more competent, and better leaders than low-competence managers, regardless of AI gender. In contrast, when the AI manager was visually represented, competence cues had attenuated influence once facial information was present. Instead, participants showed systematic gender-differentiated responses to AI faces, with feminine-appearing managers evaluated as more competent and more trustworthy than masculine-appearing managers, particularly when delivering favourable outcomes. These gender effects were largely absent when outcomes were unfavourable, suggesting that negative feedback attenuates the influence of both competence information and facial cues. Taken together, these findings show that competence information can mitigate negative reactions to AI managers in text-based interactions, whereas facial anthropomorphism elicits gendered perceptual biases not observed in text-only settings. The results highlight that representational modality plays a critical role in determining when gender stereotypes are activated in evaluations of AI systems and underscore that design choices are consequential for AI governance in evaluative contexts.

翻译：本研究探讨能力线索能否减少对AI管理者评价中的性别偏见，以及这些效应是否取决于AI的呈现方式。通过两项预注册实验（N = 2,505），采用2×2×3设计（操纵AI性别、能力与决策结果），我们比较了基于文本描述的AI管理者与使用反向关联范式生成的视觉化AI面孔。在文本条件下，评价主要由能力驱动而非性别。当参与者收到不利决策时，高能力AI管理者被认为比低能力管理者更公平、更有能力、更具领导力，且与AI性别无关。相反，当AI管理者以视觉形式呈现时，一旦面部信息存在，能力线索的影响便减弱。参与者对AI面孔表现出系统性的性别差异化反应：女性化外貌的管理者被认为比男性化外貌的管理者更有能力且更可信，尤其在传递有利结果时。当结果不利时，这些性别效应基本消失，表明负面反馈会削弱能力信息与面部线索的影响。综上所述，这些发现表明：在基于文本的交互中，能力信息可缓解对AI管理者的负面反应；而面部拟人化则会引发在纯文本环境中未观察到的性别化感知偏差。结果凸显呈现模态在决定AI系统评价中性别刻板印象何时被激活的关键作用，并强调设计选择在评估情境中对AI治理具有重要影响。