Racial Biases in Officers’ Decisions to Frisk Are Amplified for Black People Stopped Among Groups Leading to Similar Biases in Searches, Arrests, and Use of Force
Bibliographic record
Abstract
Violent encounters between police and Black people have spurred debates about how race affects officer decision-making. We propose that racial disparities in police–civilian interactions are amplified when police interact with Black civilians who are encountered in groups. To test this possibility, we analyzed New York City stop and frisk data for over 2 million police stops. Results revealed that Black (vs. White) people were more likely to be frisked, searched, arrested, and have force used against them. Critically, these racial disparities were more pronounced for people stopped in groups (vs. alone): Being stopped in a group led to a 1.7% increase in racial disparities for frisks, a 1% increase for searches, a 0.3% increase for arrests, and a 1.7% increase for use of force. Moreover, these disparities held even when we controlled for a potential proxy of effective policing: discovery of illegal contraband. We conclude that groups amplify racial disparities in policing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".