Toward Governance-Aware Autonomous GIS: A Narrative Review of Ethical and Privacy Risks in LLM-Enabled GeoAI

面向治理感知的自主地理信息系统：大语言模型赋能地理人工智能（GeoAI）中伦理与隐私风险的叙述性综述

Abstract: 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.

摘要： 由大语言模型（LLM）驱动的地理人工智能（GeoAI）正在通过自然语言界面和代理式自主地理信息系统（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中尚未完全涵盖的治理挑战，包括从移动轨迹中进行被动位置推断、由空间自相关和尺度效应驱动的空间结构化偏见放大、空间事实幻觉，以及多模态地理空间输入中不确定性的叠加。

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.

本叙述性综述确定了LLM赋能的GeoAI中八个反复出现的问题：数据来源与授权、空间隐私与推断风险、算法偏见与空间不平等、作为结构性风险的空间机制（空间自相关、可变面元问题及尺度效应）、LLM特有的技术风险、可解释性、政策与监管缺口，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，结合文献中的实例进行论证，并评估了当前技术或制度层面的应对现状，涵盖了从尚未解决到积极讨论或受制于新兴政策的各种状态。

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.

基于上述综合分析，我们提出了一种面向LLM赋能自主GIS的治理感知架构，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措施和可审计工件，并通过一个洪水响应路径规划场景进行了演示。本综述强调了一个持续存在的证据缺口：目前提出的应对方案大多仍处于概念阶段，针对LLM赋能GeoAI治理控制的实地测试评估依然有限。最后，我们概述了一项研究议程，强调实证验证、空间特异性可解释性工具，以及与这些新兴风险相适应的劳动力培训。