算法判断的地理：LLM中介、地方身份与住房搜索中的种族引导
The Geography of Algorithmic Judgment: LLM Intermediaries, Place Identity, and Racial Steering in Housing Search
AI总结：
通过行为审计七种LLM在四个美国城市的住房推荐，发现种族引导是模型解释性许可的涌现行为，而非静态属性，且城市并非中性测试单元。
摘要：
大型语言模型（LLM）正迅速在住房搜索中扮演中介角色，通过将列表平台集成到对话界面中，调解城市环境中的信息访问、搜索和推荐。我们扩展了先前关于LLM中种族引导的研究，对七个开放权重和闭源LLM在四个美国城市进行了行为审计，测试了三种迭代提示条件下的位置推荐，这些条件逐步添加生活方式偏好背景并反映公平住房配对测试方法。我们发现，引导是模型解释性许可的涌现行为，而非主要静态属性。引导源于用户身份、偏好表达以及模型内化的关于特定城市中地点、偏好和机会的学习表征的空间逻辑之间的相互作用，以及不同类型用户如何与之关联。虽然存在引导，但在评估条件下其方向和幅度并不一致。偏好条件测试通常增加或重新配置了相对于基线条件表现出引导行为的模型数量，表明LLM可能根据用户的种族身份对相同的住房偏好做出不同解释。我们的发现还表明，在基于地点的领域进行LLM评估时，城市并非中性测试单元，一个本地市场的结果不能假定推广到另一个市场。在住房领域，需要本地和领域专业知识，以确保法律和制度对公平住房的承诺不会因采用调解空间访问的AI工具而受到损害。
英文摘要：
Large language models (LLMs) are rapidly assuming an intermediary role in housing search through the integration of listing platforms within conversational interfaces, mediating access to information, search, and recommendations within urban settings. We expand on prior work on racial steering in LLMs by conducting a behavioral audit of seven open-weight and closed-source LLMs across four U.S. cities, testing location recommendations across three iterative prompting conditions that progressively add lifestyle preference context and reflect fair housing paired-testing methodologies. We find that steering is an emergent behavior of the model's interpretive license rather than primarily a static property. Steering results from the interaction of a user's identity, preference articulation, and the spatial logic that a model has internalized about learned representations of place, preference, and opportunity in a given city, and how different types of users relate to it. While steering was present, it was not uniform in direction or magnitude across evaluated conditions. Preference-conditioned testing often increased or reconfigured the number of models that exhibited steering behaviors relative to baseline conditions, suggesting that LLMs may interpret what the same housing preference means differently depending on the racial identity of the user. Our findings also demonstrate that the city is not a neutral testing unit for LLM evaluation in place-based sectors, and results from one local market cannot be assumed to generalize to another. Local and domain expertise will be required in the housing sector to ensure that legal and institutional commitments to fair housing are not undermined while adopting AI tools that mediate spatial access.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
