Operationalizing Perceptions of Agent Gender: Foundations and Guidelines

A team of six researchers, led by Katie Seaborn, has published a groundbreaking paper that tackles a critical but often overlooked variable in human-AI interaction: the perceived gender of intelligent agents. Their work, 'Operationalizing Perceptions of Agent Gender: Foundations and Guidelines,' is slated for presentation at the prestigious CHI 2026 conference. The study conducts a scoping review of existing research, uncovering a significant methodological gap: approximately one-third of studies on agent gender manipulated this variable (e.g., giving a chatbot a male or female voice) but failed to measure how users actually perceived it or how those perceptions influenced outcomes like trust, preference, or toxicity.

This lack of standardized operationalization—clear definitions, labeling, and measurement—limits the field's ability to compare studies, conduct meaningful meta-analyses, and build coherent knowledge. The researchers argue that the field has been constrained by a dominant gender binary model and latent anthropocentrism, which arbitrarily limit design possibilities and reinforce the status quo. In response, they contribute a systematically developed, theory-driven framework designed to bring greater rigor, clarity, and inclusivity to future research and development of AI agents, social robots, and virtual characters.