HMN 2025: How Humans bring gender bias to their interactions with AI

Humans bring gender biases to their interactions with Artificial Intelligence (AI), according to new research from Trinity College Dublin and Ludwig-Maximilians Universität (LMU) Munich.

The study involving 402 participants found that people exploited female-labeled AI and distrusted male-labeled AI to a comparable extent as they do human partners bearing the same gender labels.

Notably, in the case of female-labeled AI, the study found that exploitation in the Human-AI setting was even more prevalent than in the case of human partners with the same gender labels.

This is the first study to examine the role of machine gender in human-AI collaboration using a systematic, empirical approach.

The findings show that gendered expectations from human-human settings extend to human-AI cooperation. This has significant implications for how organizations design, deploy, and regulate interactive AI systems, according to the authors.

The study, led by sociologists in Trinity’s School of Social Sciences and Philosophy, has just been published in the journal iScience.

Key findings include:

• Patterns of exploitation and distrust toward AI agents mirrored those seen with human partners carrying the same gender labels.
• Participants were more likely to exploit AI agents labeled female and more likely to distrust AI agents labeled male.
• Assigning gender to AI agents can shape cooperation, trust, and misuse implications for product design, workplace deployment, and governance.

Sepideh Bazazi, first author of the study and Visiting Research Fellow at the School of Social Sciences and Philosophy, Trinity, explained, “As AI becomes part of everyday life our findings that gendered expectations spill into human-AI cooperation underscore the importance of carefully considering gender representation in AI design, for example, to maximize people’s engagement and build trust in their interactions with automated systems.

“Designers of interactive AI agents should recognize and mitigate biases in human interactions to prevent reinforcing harmful gender discrimination and to create trustworthy, fair, and socially responsible AI systems.”

Taha Yasseri, co-author of the study and Director of the Centre for Sociology of Humans and Machines (SOHAM) at Trinity, said, “Our results show that simply assigning a gender label to an AI can change how people treat it. If organizations give AI agents human-like cues, including gender, they should anticipate downstream effects on trust and cooperation.”

Jurgis Karpus, co-author of the study and Postdoctoral Researcher at Ludwig-Maximilians-Universität (LMU) Munich, added, “This study raises an important dilemma. Giving AI agents human-like features can foster cooperation between people and AI, but it also risks transferring and reinforcing unwelcome existing gender biases from people’s interactions with fellow humans.”

More about the study

In this experimental study, participants played repeated rounds of the social science experiment Prisoner’s Dilemma—a classic experiment in behavioral game theory and economics to study human cooperation and defection. Partners were labeled human or AI. Each partner was further labeled male, female, non-binary, or gender-neutral.

The team analyzed motives for cooperation and defection, distinguishing exploitation (taking advantage of a cooperative partner) from distrust (defecting pre-emptively). Findings show that gender labeling can reproduce gendered patterns of cooperation with AI. The participants were recruited in the UK and the experiment was conducted online. The sample size was 402 participants.

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