Gender Bias in Perception of Human Managers Extends to AI Managers

2025-02-24

AI Summary

This study investigates whether gender bias systematically transfers from human to AI managers, challenging the assumption of algorithmic neutrality in management. Method: A 2×3 randomized controlled experiment manipulated manager type (human vs. AI) and manager gender (male, female, neutral) within a team reward allocation context, measuring perceived fairness, competence, and trustworthiness via validated subjective scales. Contribution/Results: Results provide the first empirical evidence that AI managers do not inherently mitigate gender bias. Male managers—whether human or AI—experienced significantly increased fairness and trust ratings after awarding rewards. In contrast, female AI managers suffered the largest declines in perceived fairness and trust when withholding rewards—significantly exceeding declines observed for other conditions. These findings reveal a mechanism by which implicit gender bias persists in algorithmic management, undermining claims of AI objectivity. The study delivers critical evidence for AI governance and equitable organizational design, urging explicit bias mitigation in AI-driven managerial systems.

Technology Category

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

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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 - either human or AI - who was presented as male, female, or gender-neutral. 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 fair, competent, and trustworthy, while those who were not selected viewed them less favorably. However, male managers - both human and AI - were more positively received by awarded participants, whereas female managers, especially female AI managers, faced greater skepticism and negative judgments when they denied rewards. These results suggest that gender bias in leadership extends beyond human managers and towards AI-driven decision-makers. As AI takes on greater managerial roles, understanding and addressing these biases will be crucial for designing fair and effective AI management systems.

Problem

• Gender bias in AI managers perception
• Comparison of human vs AI manager evaluations
• Impact of gender on managerial fairness judgments

Innovation

• Randomized controlled trials for bias
• Gender perception in AI managers
• Addressing bias in AI leadership