Attitudes of polices officers towards algorithmic discretion: Empirical evidence from the UK

Research Institution / Organisation

Queen Mary University of London

Principal Researcher

Muhammad Afzal

Level of Research

PhD

Project Start Date

May 2021

Research Context

​This research aims to investigate the views of UK police officers on the use of big data applications and algorithms in policing.

One of the major causes of resistance to implementation of big data applications and algorithms is that their use may affect the discretion exercised by police officers. Scholars are divided as to the nature of this effect – whether it enhances their discretion or curtails it. This research attempts to unearth this dichotomy by evaluating the significance the police officers attach to discretion and the use of data and algorithms in their routines and practices. The findings will help the police policy makers and managers to devise appropriate policies for effective implementation of big data applications and algorithms.

Research Methodology

The study is following a quantitative approach. Data will be collected through an online survey from police officers of different ranks working in police organisations / departments across the United Kingdom who have experience of using big data and algorithmic tools. Structural equation modelling will be used to analyse the attitudes of police officers towards use of big data application and algorithms.

As a part of my research, I am conducting an online survey , that takes approximately 8-10 minutes for completion and responses are completely anonymised. We request the police officers of all ranks who have experience of using big data and algorithmic tools (like predictive policing, geospatial/temporal crime mapping software like MapInfo, ANPRs, facial recognition technology, automated decision systems for case assessment, granting firearms' licenses, road traffic management etc) to participate in the survey by clicking on the link below:
Survey link: https://qmulbusiness.qualtrics.com/jfe/form/SV_2csND8nQ9j2HR2e  

Date due for completion

December 2022
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