Visual Anthropomorphism Shifts Evaluations of Gendered AI Managers

2026-02-20

AI Summary

This study investigates how gender bias influences perceptions of competence and fairness in AI managers across textual and visual modalities of representation. Through two preregistered experiments employing a 2×2×3 design that manipulates AI gender, competence level, and decision outcomes, the authors use a reverse correlation paradigm to generate facial images for visual anthropomorphism. Results reveal that evaluations in the textual condition are primarily driven by competence cues, whereas the visual condition significantly activates gender stereotypes: feminized faces are attributed higher competence and trustworthiness following positive outcomes, while negative outcomes diminish the impact of both competence and facial cues. This work provides the first evidence that representational modality critically moderates the activation of bias, highlighting the unique role of visual anthropomorphism in shaping social cognition of AI agents.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Computer Vision: Bias, Fairness & Privacy
• Humans and AI: Learning Human Values and Preferences

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

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.

Problem

• gender bias
• visual anthropomorphism
• AI managers
• competence cues
• evaluative judgments

Innovation

• visual anthropomorphism
• gender bias
• AI evaluation
• competence cues
• representational modality