Gender Bias in Perception of Human Managers Extends to AI Managers

TL;DR

This study investigates how gender biases influence perceptions of AI managers compared to human managers, revealing that biases extend to AI and affect trust and fairness judgments.

Contribution

It provides experimental evidence that gender bias in leadership perceptions applies to AI managers, highlighting implications for designing fair AI management systems.

Findings

Participants' perceptions changed after award decisions.

Male managers received more positive evaluations regardless of being human or AI.

Female AI managers faced skepticism, especially when not awarding team members.

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 notably after experiencing the award process. As expected, those who received awards rated their managers as more trustworthy, competent, and fair, and they were more willing to work with similar managers in the future. In contrast, 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 well. As AI assumes more managerial responsibilities, understanding and addressing these biases will be crucial for designing fair and effective AI management systems.