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

Location data reveals sensitive information beyond what people realize: LLMs can infer private details about individuals from mobility traces (movement patterns) without explicit consent, creating privacy risks that standard data protection frameworks don't adequately address.

Spatial bias gets amplified in ways unique to geography: Because neighboring areas tend to be similar (spatial autocorrelation), AI systems trained on biased data can systematically disadvantage entire regions or communities, and changing the geographic scale of analysis can completely change results—a problem called the modifiable areal unit problem.

LLMs confidently generate false geographic information: These systems can "hallucinate" spatial facts (like inventing details about places or relationships between locations) while sounding authoritative, and errors compound when combining multiple types of geographic data sources.

Current policies and regulations lag behind the technology: Existing AI ethics guidelines and privacy laws weren't designed for geospatial AI's specific challenges, leaving significant governance gaps around how location data should be used and protected.

We need geographic-specific safeguards built into the system design: Rather than applying general AI ethics principles, the paper proposes that responsible LLM-enabled GIS requires controls specifically mapped to each stage of the geospatial data lifecycle, with auditable records to track decisions and potential harms.