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A Demographic Model to Predict Arrests by Race: An Exploratory Approach

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1445)


​​Racial discrimination remains a critical issue in the United States. Hence, the study employs a logistic regression model to predict racial arrests based on demographics. Using a secondary open dataset from the City of Albany’s official website from November 2020 to September 27, 2021, the results revealed that the variables with relative influence on the race of an individual arrested are the age, neighborhood, and sex variables, at 80%, 12%, and 8%, respectively. The model results demonstrated that the likelihood of a white male between the age of 18 and 25 years arrested in a predominantly white neighborhood is significantly less than being black. Limitations of the study include small sample size (10 months of historical data); and under-specification of the Logistic regression model due to excluding one or more relevant independent variables from the model.


  • Race
  • United States
  • Racial discrimination
  • Racial profiling
  • Neighborhoods
  • Policing
  • Predictive modeling
  • Supervised learning
  • Logistic regression
  • Open data

Authors Alice Nneka Ottah and Yvonne Appiah Dadson have contributed equally to this work.

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Ottah, A.N., Dadson, Y.A., Caramancion, K.M. (2023). A Demographic Model to Predict Arrests by Race: An Exploratory Approach. In: Latifi, S. (eds) ITNG 2023 20th International Conference on Information Technology-New Generations. ITNG 2023. Advances in Intelligent Systems and Computing, vol 1445. Springer, Cham.

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-28331-4

  • Online ISBN: 978-3-031-28332-1

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