提出智能体性别感知的标准化框架，提升研究可比性与包容性
Operationalizing Perceptions of Agent Gender: Foundations and Guidelines
- 构建理论驱动的元层级框架，明确性别感知的操作化路径
- 发现三分之一研究仅操控未测量性别，缺乏统一标准
- 推动打破性别二元范式，促进更包容的智能体设计
智能体、虚拟角色、社交机器人等代理系统的'性别'已成为人机交互研究的核心议题。用户对智能体性别的感知可解释其态度与行为（如偏好、毒性、刻板印象），但当前缺乏统一的性别感知测量标准。一项范围综述揭示：约三分之一的研究操纵了智能体性别却未实际测量；现有操作化方式模糊不清，阻碍结果理解、测量一致性及元分析比较。性别二元模型主导与隐含的人类中心主义限制了知识生成并固化现状。本文提出系统构建、理论驱动的元层级框架，为提升研究严谨性与包容性提供实践指导。
原文摘要 · Abstract (English)
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.
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