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Analyzing Societal Bias of California Police Stops Through Lens of Data Science

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Intelligent Computing

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 284))

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Abstract

The Police Department in any city uses a wide range of enforcement to ensure public safety and traffic stops are one such tool. During this process, racial disparities in policing in the United States is not very uncommon. This has created both social and ethical concerns among people. As a result researchers in different domains have taken interest in connecting different pieces of information and finding a possible solution to these issues. In this paper, we have addressed the societal bias through the lens of data science focusing on how police stops are made and which groups of people are targeted most often. The analysis is done on major cities of California focusing on the different factors that might create discrimination in police stops. We also used two popularly known statistical analysis benchmark test and veil of darkness to look into racial profiling. Based on our analysis, it was clear that the Black drivers were stopped between 2.5 to 3.7 times more than white drivers and Hispanic drivers were stopped between 0.968 to 1.39 times more than white drivers (between 2014 and 2017 respectively).

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Notes

  1. 1.

    https://openpolicing.stanford.edu/data/.

  2. 2.

    https://cran.r-project.org/web/packages/suncalc/suncalc.pdf.

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Author information

Authors and Affiliations

  1. Santa Clara University, Santa Clara, CA, 95053, USA

    Sukanya Manna & Sara Bunyard

Authors
  1. Sukanya Manna
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  2. Sara Bunyard
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Corresponding author

Correspondence to Sukanya Manna .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

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Manna, S., Bunyard, S. (2021). Analyzing Societal Bias of California Police Stops Through Lens of Data Science. In: Arai, K. (eds) Intelligent Computing. Lecture Notes in Networks and Systems, vol 284. Springer, Cham. https://doi.org/10.1007/978-3-030-80126-7_9

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