Operationalizing Perceptions of Agent Gender: Foundations and Guidelines
Summary: arXiv:2603.26682v1 Announce Type: cross
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.
This article contributes a systematically-developed and theory-driven meta-level framework that offers operational clarity and practical guidance for greater rigour and inclusivity.
Key Findings
- Significance of Agent Gender: Understanding the gender perceptions of intelligent agents is essential for improving user interaction and satisfaction.
- Operationalization Challenges: Many studies manipulate agent gender without adequately measuring it, leading to inconsistencies in research results.
- Binary Limitations: The prevalent use of a binary gender model restricts the scope of research and reinforces existing biases.
- Framework Contribution: The proposed framework focuses on enhancing the measurement and definition of agent gender, promoting inclusivity.
Implications for Future Research
The findings from this review suggest several directions for future research in the field of human-agent interaction:
- Standardization of Measurements: Developing standardized instruments to measure perceptions of agent gender could enhance the comparability of research across studies.
- Expanding Gender Definitions: Researchers should consider non-binary and culturally diverse definitions of gender to capture a broader range of perceptions.
- Longitudinal Studies: Future studies could look into how perceptions of agent gender evolve over time as societal norms change.
- User-Centric Design: Implementing insights from gender perception studies can lead to the design of more engaging and effective intelligent agents.
Conclusion
As the field of human-agent interaction continues to evolve, it is crucial to operationalize perceptions of agent gender rigorously. By addressing the existing gaps in research and promoting an inclusive framework, we can better understand how users interact with intelligent agents and ultimately enhance the user experience across various technological platforms.