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A data-driven analysis of the interplay between criminological theory and predictive policing algorithms

A data-driven analysis of the interplay between criminological theory and predictive policing algorithms
A data-driven analysis of the interplay between criminological theory and predictive policing algorithms
Previous studies have focused on the biases and feedback loops that occur in predictive policing algorithms. These studies show how systemically and institutionally biased data leads to these feedback loops when predictive policing algorithms are applied in real life. We take a step back, and show that the choice in algorithm can be embedded in a specific criminological theory, and that the choice of a model on its own even without biased data can create biased feedback loops. By synthesizing “historical” data, in which we control the relationships between crimes, location and time, we show that the current predictive policing algorithms create biased feedback loops even with completely random data. We then review the process of creation and deployment of these predictive systems, and highlight when good practices, such as fitting a model to data, “go bad” within the context of larger system development and deployment. Using best practices from previous work on assessing and mitigating the impact of new technologies, we highlight where the design of these algorithms has broken down. The study also found that multidisciplinary analysis of such systems is vital for uncovering these issues and shows that any study of equitable AI should involve a systematic and holistic analysis of their design rationalities.
ACM Press
Chapman, Age
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Ugwudike, Pamela
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Grylls, Philip
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Gammack, David
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Ayling, Jacqueline, Anne
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Chapman, Age
721b7321-8904-4be2-9b01-876c430743f1
Ugwudike, Pamela
2faf9318-093b-4396-9ba1-2291c8991bac
Grylls, Philip
dff3e462-df6d-46cd-8366-91a75abc9a9e
Gammack, David
65b60f25-7546-4132-99da-e140f4727f8d
Ayling, Jacqueline, Anne
11c61a24-f8dc-4539-b8c4-c9edcd090111

Chapman, Age, Ugwudike, Pamela, Grylls, Philip, Gammack, David and Ayling, Jacqueline, Anne (2022) A data-driven analysis of the interplay between criminological theory and predictive policing algorithms. In ACM Conference on Fairness, Accountability, and Transparency: FaCCT. ACM Press. 14 pp .

Record type: Conference or Workshop Item (Paper)

Abstract

Previous studies have focused on the biases and feedback loops that occur in predictive policing algorithms. These studies show how systemically and institutionally biased data leads to these feedback loops when predictive policing algorithms are applied in real life. We take a step back, and show that the choice in algorithm can be embedded in a specific criminological theory, and that the choice of a model on its own even without biased data can create biased feedback loops. By synthesizing “historical” data, in which we control the relationships between crimes, location and time, we show that the current predictive policing algorithms create biased feedback loops even with completely random data. We then review the process of creation and deployment of these predictive systems, and highlight when good practices, such as fitting a model to data, “go bad” within the context of larger system development and deployment. Using best practices from previous work on assessing and mitigating the impact of new technologies, we highlight where the design of these algorithms has broken down. The study also found that multidisciplinary analysis of such systems is vital for uncovering these issues and shows that any study of equitable AI should involve a systematic and holistic analysis of their design rationalities.

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Published date: 21 December 2022

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Local EPrints ID: 457426
URI: http://eprints.soton.ac.uk/id/eprint/457426
PURE UUID: d0d665d1-8d90-4273-a21b-439be29e0a7a
ORCID for Age Chapman: ORCID iD orcid.org/0000-0002-3814-2587
ORCID for Pamela Ugwudike: ORCID iD orcid.org/0000-0002-1084-7796
ORCID for Philip Grylls: ORCID iD orcid.org/0000-0001-9677-5852
ORCID for David Gammack: ORCID iD orcid.org/0000-0003-1214-1057

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Date deposited: 07 Jun 2022 16:55
Last modified: 06 Jan 2023 02:46

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Contributors

Author: Age Chapman ORCID iD
Author: Pamela Ugwudike ORCID iD
Author: Philip Grylls ORCID iD
Author: David Gammack ORCID iD
Author: Jacqueline, Anne Ayling

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