Texas High School ELA Question of the Day

Add a case study from a large city: an internal review found that over 80% of people flagged as high risk did not commit a violent offense the following year; patrol hours doubled in neighborhoods with historically high stop rates regardless of current complaint data; after public hearings, the city required quarterly bias audits comparing model outputs to independent victimization surveys and published thresholds showing how adjusting false-positive rates shifted patrol routes.

Insert a discussion of controversies surrounding facial recognition accuracy across demographic groups, including technical debates about image resolution and training datasets in airports and retail settings.

Suggest adding examples from several cities to show different perspectives and remind readers that every community is unique in how it uses technology.

Reiterate that feedback loops occur when data about past enforcement feed future deployments, emphasizing again that objectivity can mask bias and reduce accountability.