面向治理感知的自主GIS：LLM赋能的GeoAI中伦理与隐私风险综述

中文摘要 AI 辅助

由大语言模型（LLM）驱动的地理空间人工智能（GeoAI）正在通过自然语言接口和智能体自主GIS工作流扩展查询、生成和解释空间信息的能力。这种能力带来了通用人工智能伦理讨论未能完全涵盖的治理挑战，包括从移动轨迹中被动推断位置、由空间自相关和尺度效应驱动的空间结构化偏差放大、幻觉性空间事实，以及跨多模态地理空间输入的不确定性累积。本综述识别了LLM赋能的GeoAI中八个反复出现的问题：数据来源与同意、空间隐私与推断风险、算法偏差与空间不平等、作为结构性风险的空间机制（空间自相关、可变面元问题和尺度效应）、LLM特定技术风险、可解释性、政策与监管空白，以及公众赋能与劳动力发展。针对每个问题，我们描述了其潜在机制，用文献中的实例加以说明，并评估了技术或制度响应的现状，范围从基本未解决到积极辩论或受新兴政策约束。基于这一综合，我们提出了一种面向治理感知的LLM赋能自主GIS架构，将每个问题映射到地理空间数据生命周期中的可执行控制和可审计工件，并通过一个洪灾响应路径规划的实例加以说明。本综述强调了一个持续存在的证据缺口：所提出的响应大多仍停留在概念层面，针对LLM赋能GeoAI治理控制的实地测试评估仍然有限。最后，我们概述了一个研究议程，强调实证验证、空间特定的可解释性工具，以及与这些新兴风险相一致的劳动力培训。

英文摘要

Geospatial artificial intelligence (GeoAI) powered by large language models (LLMs) is expanding the capacity to query, generate, and interpret spatial information through natural-language interfaces and agentic autonomous GIS workflows. This capability creates governance challenges that general AI ethics discussions do not fully capture, including passive location inference from mobility traces, spatially structured bias amplification driven by spatial autocorrelation and scale effects, hallucinated spatial facts, and uncertainty compounding across multimodal geospatial inputs. This narrative review identifies eight recurring issues in LLM-enabled GeoAI: data provenance and consent, spatial privacy and inference risk, algorithmic bias and spatial inequity, spatial mechanisms as structural risk (spatial autocorrelation, the modifiable areal unit problem, and scale effects), LLM-specific technical risks, explainability, policy and regulatory gaps, and public enablement and workforce development. For each issue, we characterize the underlying mechanism, ground it in an illustrative example from the literature, and assess the current state of technical or institutional responses, ranging from largely unaddressed to actively debated or subject to emerging policy. Building on this synthesis, we propose a governance-aware architecture for LLM-enabled autonomous GIS that maps each issue to enforceable controls and auditable artifacts across the geospatial data lifecycle, illustrated through a worked flood-response routing scenario. The review highlights a persistent evidence gap: proposed responses remain largely conceptual, and field-tested evaluations of governance controls for LLM-enabled GeoAI remain limited. We close by outlining a research agenda emphasizing empirical validation, spatially specific interpretability tools, and workforce training aligned with these emerging risks.

发表机构

• Harvard T.H. Chan School of Public Health（哈佛陈曾熙公共卫生学院）
• Center for Geographic Analysis (CGA), Harvard University（哈佛大学地理分析中心（CGA））

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