Operationalizing Perceptions of Agent Gender: Foundations and Guidelines

TL;DR

This paper reviews how perceptions of agent gender are measured in studies, highlights inconsistencies and limitations, and proposes a comprehensive framework for more rigorous and inclusive operationalization.

Contribution

It introduces a systematic, theory-driven framework to improve the clarity, consistency, and inclusivity of operationalizing agent gender perceptions in research.

Findings

One-third of studies manipulated but did not measure agent gender.

Current operationalizations are inconsistent and often binary.

The framework promotes inclusivity beyond binary gender models.

Abstract

The "gender" of intelligent agents, virtual characters, social robots, and other agentic machines has emerged as a fundamental topic in studies of people's interactions with computers. Perceptions of agent gender can help explain user attitudes and behaviours -- from preferences to toxicity to stereotyping -- across a variety of systems and contexts of use. Yet, standards in capturing perceptions of agent gender do not exist. A scoping review was conducted to clarify how agent gender has been operationalized -- labelled, defined, and measured -- as a perceptual variable. One-third of studies manipulated but did not measure agent gender. Norms in operationalizations remain obscure, limiting comprehension of results, congruity in measurement, and comparability for meta-analyses. The dominance of the gender binary model and latent anthropocentrism have placed arbitrary limits on knowledge generation and reified the status quo. We contribute a systematically-developed and theory-driven meta-level framework that offers operational clarity and practical guidance for greater rigour and inclusivity.