VR实验发现警察对黑人男性虚拟角色语气更不尊重，易引发冲突。
The Effect of Perceived Race and Gender on Police Language Use: Experimental Evidence from VR Simulations
- 通过VR模拟让警察与黑人男性虚拟角色对话，用因果分析评估语气差异。
- 多数警察对黑人男性角色减少尊重语气，尤其在对方被设定为嫌疑人时。
- 研究揭示了语言态度如何加剧警民冲突，适合关注警务公平的读者。
在美警方与公众互动暴力频发的背景下，本研究通过虚拟现实（VR）模拟，探究警察在面对被呈现为黑人成年男性的虚拟角色时，其语言态度的尊重程度变化。研究采用因果推断方法，将黑人男性角色分配给不同警察及场景作为处理变量，以边际平均处理效应（ATE）衡量该角色对警察每轮对话中语气尊重度的影响。结果显示，多数警察对黑人男性角色表现出较低的尊重语气，但白人、跨种族及多族裔女性警察例外，尤其在虚拟角色被设定为嫌疑人的情境下更为明显。在整个典型对话过程中，这种边际ATE可导致语气尊重度出现2至数个百分点的显著差异（量表0-10），超出初始感知黑人男性的影响。更令人担忧的是，这种语言态度可能引发对话破裂，进而增加暴力或危险风险。此外，研究还评估了大语言模型（LLMs）在估计ATE中的应用能力。通过与合成数据对比，研究建议在具有文本的多层次数据中，使用混合效应模型结合逆概率加权（IPTW）方法，并利用LLM生成文本特征。尽管尝试用LLM微调预测模型以估算ATE，但结论认为该方向仍需进一步发展。
原文摘要 · Abstract (English)
Against the backdrop of violence in police interactions with the U.S. public, we explore how deferentially police officers speak to virtual characters depicted as Black adult males in vir- tual reality (VR) simulations. We evaluate the effect of seeing and communicating with these characters through a causal in- ference lens, where the assignment of the Black man character to a police officer and simulation is the treatment variable. Our (marginal) average treatment effect AT E measures the social impact of the character on the deference of officer statements with each turn of the conversation. Soberingly, we find that most officers speak less deferentially to Black man characters, except for White, biracial, and multiracial female officers, es- pecially in settings where the VR character was known to be a suspect. Across a full conversation of a typical VR scene, these marginal AT Es can result in notable changes in def- erence of tone (two to several points difference on a scale of 0-10), above and beyond that due to the initial effect of per- ceiving a Black male character. Even more disconcerting is that this can contribute to conversation breakdowns that po- tentially result in violence or danger to both the public and the police. We also explored the capabilities of large language models (LLMs) for ATE estimation. From our methods com- parison analysis, including model validation against synthetic data, we provide unique scientific insights on LLM-assisted methodologies for ATE estimation. As such, for ATE esti- mation with multilevel data with text, we recommend mixed effects models with the inverse propensity treatment weighted (iptw) approach, which utilized an LLM for text feature cre- ation. While we also tested LLMs for finetuning prediction models ultimately for ATE estimation, we conclude they are an area for further development and refinement.
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