AI Gender Problem: Researchers Expose Messy Standards in Robot Design
A major review found that AI systems and robots are assigned genders inconsistently—and often unmeasured—creating confusion about how these assignments shape user behavior and bias. The lack of common standards is blocking companies and researchers from reliably comparing results or predicting how their AI will be perceived.
Originaltitel: Operationalizing Perceptions of Agent Gender: Foundations and Guidelines
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.