The ‘doings’ behind data: An ethnography of police data construction

Public organisations, like the Netherlands Police, increasingly rely on data. Despite its importance, there has been limited empirical attention to how data is created and how its situated context impacts algorithmic interpretation of data. Using the Netherlands Police as a research focus, this study aims to fill this gap by studying datafication from an interpretivist perspective, emphasising the importance of material factors and human actors engaging in ‘data work’. The main research question is: How do material factors and human actors interact in ‘data work’ to enable datafication of street-level situated contexts at the Netherlands Police? The study builds on nearly 200 h of ethnographic fieldwork with street-level employees at the Netherlands Police. It finds that data work is deeply embedded in daily policing and is shaped by personal values, organisational context, and practical considerations. The findings highlight challenges posed by structured and unstructured data entry in the registration software for police reports. Structured data limit discretion with predefined labels, often conflicting with employees’ own perceptions. Unstructured data offer more flexibility but pose challenges such as linguistic nuance, inconsistencies, and the presence of ‘voice’ in police reports that complicate data interpretation. The study unearths various patterns of the interplay between human actors and material factors in relation to their situated contexts, which impact how police reports are constructed through a registration system. This approach, emphasising the interplay between material factors, human actors, organisational dynamics, and contextual factors, can help public sector organisations take steps towards responsible algorithmic interpretation of data.

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Publication Details

Journal
Big Data & Society
Published
2024-09-01
DOI
https://doi.org/10.1177/20539517241270695
Citations
1
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
0.53

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article

The ‘doings’ behind data: An ethnography of police data construction

Isabelle Donatz-Fest
1 citations
Big Data & Society
Ethics and Social Impacts of AI
0.53
article

The ‘doings’ behind data: An ethnography of police data construction

Isabelle Donatz-Fest
article en
1 citations

Abstract

Public organisations, like the Netherlands Police, increasingly rely on data. Despite its importance, there has been limited empirical attention to how data is created and how its situated context impacts algorithmic interpretation of data. Using the Netherlands Police as a research focus, this study aims to fill this gap by studying datafication from an interpretivist perspective, emphasising the importance of material factors and human actors engaging in ‘data work’. The main research question is: How do material factors and human actors interact in ‘data work’ to enable datafication of street-level situated contexts at the Netherlands Police? The study builds on nearly 200 h of ethnographic fieldwork with street-level employees at the Netherlands Police. It finds that data work is deeply embedded in daily policing and is shaped by personal values, organisational context, and practical considerations. The findings highlight challenges posed by structured and unstructured data entry in the registration software for police reports. Structured data limit discretion with predefined labels, often conflicting with employees’ own perceptions. Unstructured data offer more flexibility but pose challenges such as linguistic nuance, inconsistencies, and the presence of ‘voice’ in police reports that complicate data interpretation. The study unearths various patterns of the interplay between human actors and material factors in relation to their situated contexts, which impact how police reports are constructed through a registration system. This approach, emphasising the interplay between material factors, human actors, organisational dynamics, and contextual factors, can help public sector organisations take steps towards responsible algorithmic interpretation of data.

Big Data & SocietyVol. 11(3)
Utrecht University (NL)
Nederlandse Organisatie voor Wetenschappelijk Onderzoek
Openalex Percentile: Top 27%
Ethics and Social Impacts of AI
0.53
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