Digital Data and Algorithms in Law Enforcement: Some Pointers for Responsible Implementation


Author / Producer

Date

2022-11

Publication Type

Journal Article, Journal Article

ETH Bibliography

yes

Citations

Altmetric

Data

Abstract

Digital data and algorithmic tools have over the past years found their way into law enforcement contexts, including modes of biometric identification and matching, automated surveillance capacities, short-term situational predictions, AI-supported analysis for large amounts of data, and the interoperability of large-scale databases and platforms for data exchange and investigation. These tools can help to increase the effectiveness and efficiency of law enforcement operations on the strategic, tactical, and operational level. They do, however, also come with a number of concerns that must be acknowledged and addressed in order to realize their potential and avoid unintended side-effects and societal frictions. Based on a multi-year research project on predictive policing in Germany and Switzerland, this paper provides a systematic perspective on the challenges involved in implementing new and emerging technologies in law enforcement contexts. Specifically, it will address (1) the nature of data, i.e. how data are socially constructed and present a particular account of the world, inevitably leading to “biased” results; (2) transparency in algorithms and AI, i.e. how “black boxes” undercut human capacities to understand and retrace processes and create problems for public accountability; (3) automation and human control, i.e. the question how human operators can retain meaningful influence over analytical processes; (4) decision-making processes and automation bias, i.e. how humans can be empowered to critically question and override system recommendations; and (5) strategic and societal implications, i.e. the fact that digital tools should not be misused to displace larger programs that address the root causes of crime.

Publication status

published

Editor

Book title

Volume

6

Pages / Article No.

39 - 46

Publisher

CEPOL

Event

Edition / version

Methods

Software

Geographic location

Date collected

Date created

Subject

digitisation; data; algorithm; implementation; police; accountability; civil liberties

Organisational unit

09785 - Leese, Matthias / Leese, Matthias check_circle

Notes

Funding

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