Gender differences in serious police misconduct: A machine-learning analysis of the New York Police Department (NYPD)

Timothy I.C. Cubitt, Janne E. Gaub, Kristy Holtfreter

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: Despite a considerable body of research on police misconduct, findings have been mixed, with little consensus regarding its causes and best practices for prevention. Emerging research has focused on the role of gender in understanding and preventing misconduct. The current study examines the extent to which the features associated with serious misconduct differ between male and female officers. Methods: Using a unique complaint dataset from the NYPD, we apply a sequence of machine learning analytics to consider if it is possible to predict serious misconduct among either group, and whether key predictors differ between groups. Results: The results show that it was possible to predict serious misconduct among each group with considerable confidence, while there were notable differences in prevalence, and type of misconduct between sexes. Conclusions: Findings hold important implications for policy, prevention and analytical approaches to police misconduct.

Original languageEnglish (US)
Article number101976
JournalJournal of Criminal Justice
Volume82
DOIs
StatePublished - Sep 1 2022

Keywords

  • Gender
  • Machine learning
  • Misconduct
  • NYPD
  • Policing

ASJC Scopus subject areas

  • Social Psychology
  • Applied Psychology
  • Sociology and Political Science
  • Law

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