Spatial disparities in simulated patrol detection: a reproducible generative-model audit across Baltimore and Chicago
Read the original on arXiv AI →The study presents a reproducible spatial simulation audit that combines a generative adversarial network for patrol allocation with a probabilistic detection model to assess patrol detection disparities in Baltimore and Chicago. Using archived crime records and census data, the authors compare GAN-based allocation to empirical and uniform deployment across multiple random seeds, finding detection-rate differences between Black-majority and White-majority areas ranging from –2.03 to 0.75 percentage points and coverage between 1.78% and 7.50%. The audit also evaluates a conditional tabular generative model for synthetic replacement, noting that geographic consistency fails in many cases, and highlights how simulated disparities depend on observation processes, allocation policies, training budgets, and randomness.
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