Toward Governance‐Aware Autonomous GIS : A Narrative Review of Ethical and Privacy Risks in LLM ‐Enabled GeoAI
Year: 2026
Type: review
Abstract: 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 is accompanied by 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 including 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 a concrete illustrative example drawn from the literature, and assess the current state of technical or institutional response, which ranges from largely unaddressed to actively debated to the subject of emerging policy. Building on this issue‐by‐issue 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: for most issues, proposed responses remain conceptual, and field‐tested evaluations of governance controls for LLM‐enabled GeoAI are still limited. We close by outlining a research agenda emphasizing empirical validation, spatially specific interpretability tools, and workforce training aligned to these emerging risks.
Source: Transactions in GIS
DOI: 10.1111/tgis.70397
Authors: Maya Subramanian, Devika Jain
Institution: Harvard University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,800
OpenAlex ID: W7213869887