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Taking a Look at Previous Studies on Fatal Police Shootings
by The Tech Reckoning is Upon Us!June 23rd, 2024
The Tech Reckoning is Upon Us!
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Several studies have been conducted based on utilizing local crime data to explain racial disparities and differences in fatal police shootings. We use data mining and machine learning techniques to incorporate more factors rather than only crime data. This paper is available on arxiv.org under CC BY-NC-ND 4.0 DEED license.
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The Tech Reckoning is Upon Us!
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No technological innovation comes without sacrifice. The pendulum will swing back to the people! Wanna' be 501c3.
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Academic Research Paper
Part of HackerNoon's growing list of open-source research papers, promoting free access to academic material.
Authors:
(1) Yuan Wang, University of Rochester (e-mail: yuan.wang1@simon.rochester.edu);
(2) Yangxin Fan, University of Rochester (e-mail: yfan24@ur.rochester.edu).
Table of Links
WP fatal police shooting dataset insight
Fatal police shooting rate and victims race prediction
2. Related work
Several studies have been conducted based on utilizing local crime data to explain racial disparities and differences in fatal police shootings. Mentch (2020) [8] implemented resampling procedures to take factors like local arrest demography and law enforcement density into account. He found substantially less racial disparity after accounting for local arrest demographics.
On the contrary, Ross (2015) [9] built a multi-level Bayesian model to investigate the extent of racial bias in the recent shooting of civilians by police. He concluded that racial discrimination observed in police shootings is not explainable due to local-level race-specific crime rates. Noticeably, Mentch and Ross had reached contradictory conclusions. But they inspired us to use data mining and machine learning techniques to incorporate more factors rather than only crime data to understand fatal police shootings in the US better.
This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license.
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