Gender Bias in Perception of Human Managers Extends to AI Managers

• URL: http://arxiv.org/abs/2502.17730v2
• Date: Wed, 06 Aug 2025 14:16:40 GMT
• Title: Gender Bias in Perception of Human Managers Extends to AI Managers
• Authors: Hao Cui, Taha Yasseri,
• Abstract summary: As AI becomes more embedded in workplaces, it is shifting from a tool for efficiency to an active force in organizational decision-making.<n>How AI managers are perceived in comparison to human managers and how gender influences these perceptions remains uncertain.<n>We conducted randomized controlled trials (RCTs) where teams of three participants worked together under a randomly assigned manager.
• Score: 2.5063318977668465
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As AI becomes more embedded in workplaces, it is shifting from a tool for efficiency to an active force in organizational decision-making. Whether due to anthropomorphism or intentional design choices, people often assign human-like qualities, including gender, to AI systems. However, how AI managers are perceived in comparison to human managers and how gender influences these perceptions remains uncertain. To investigate this, we conducted randomized controlled trials (RCTs) where teams of three participants worked together under a randomly assigned manager. The manager was either a human or an AI and was presented as male, female, or gender-unspecified. The manager's role was to select the best-performing team member for an additional award. Our findings reveal that while participants initially showed no strong preference based on manager type or gender, their perceptions changed significantly after experiencing the award process. As expected, those who received awards rated their managers as more trustworthy, competent, fair, and were more willing to work with similar managers in the future, while those who were not selected viewed them less favorably. However, male managers, whether human or AI, were more positively received by awarded participants, whereas female managers, especially female AI managers, faced greater skepticism and negative judgments when they did not give awards. These results suggest that gender bias in leadership extends beyond human managers to include AI-driven decision-makers. As AI assumes more managerial responsibilities, understanding and addressing these biases will be crucial for designing fair and effective AI management systems.

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