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As the 2026 midterm elections approach, gerrymandering has once again entered public and scholarly debate. For GIScience (GISc), political geography, and spatial justice research, gerrymandering has never been simply a technical question of how to draw district lines. It is about how spatial boundaries intentionally shape political representation, community (non)visibility, public resource allocation, and the ways people are governed and made visible. GISc has transformed redistricting, where GISc is a “double-edged sword” for gerrymandering. GISc can be used to create highly targeted gerrymanders, but it can also be used to detect and challenge them. Moreover, in the age of AI Everywhere, this long-standing issue is becoming even more complex: AI may not only change how redistricting and boundary manipulation are carried out but also reshape the spatial logic of political governance itself.
This discussion will begin with the impact of GISc and AI on gerrymandering and redistricting. In recent years, machine learning, nonparametric statistical learning, and algorithm-assisted redistricting have been used to generate and evaluate alternative districting plans, helping identify anomalous bias, explain district structures, and improve transparency in redistricting analysis (Stolicki et al., 2024). At the same time, we need to ask: Could AI also be used to create more refined and less visible forms of boundary manipulation? If district boundaries have already distorted community representation and public data, might AI-mediated governance further amplify these distortions? In the age of AI Everywhere, is gerrymandering expanding from a manipulation of electoral boundaries into a spatial infrastructure problem that shapes data, representation, public resources, and algorithmic governance? And how might we use GeoAI not only to detect manipulation, but also to advance spatial justice, democratic representation, and responsible AI governance?
