Datafied child welfare services: unpacking politics, economics and power
Open accessAbstract
This article analyses three distinct child welfare data systems in England. We focus on child welfare as a contested area in public services where data systems are being used to inform decision-making and transforming governance. We advance the use of “data assemblage” as an analytical framework to detail how key political and economic factors influence the development of these data systems. We provide an empirically grounded demonstration of why child welfare data systems must not be considered neutral decision aid tools. We identify how systems of thought, ownership structures, policy agendas, organizational practices, and legal frameworks influence these data systems. We find similarities in the move toward greater sharing of sensitive data, but differences in attitudes toward public-private partnerships, rights and uses of prediction. There is a worrying lack of information available about the impacts of these systems on those who are subject to them – particularly in relation to predictive data systems. We argue for policy debates to go beyond technical fixes and privacy concerns to engage with fundamental questions about the power dynamics and rights issues linked to the expansion of data sharing in this sector as well as whether predictive data systems should be used at all.
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Public-sector algorithms have drawn criticism for worse outcomes, racial bias, and structural inequality in strained, underfunded systems.
“Consequently, they have also been scrutinized for achieving worse outcomes, exacerbating racial biases, and strengthening structural inequalities [42,101,107] within systems that are overburdened and under-resourced, yet critically needed [33,75].”
From A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed through a Case Study of Child-Welfare (2021) · cites this paper as [101]
STS scholars have used data assemblages to study how bureaucratic processes and algorithmic decision-making intersect by examining the politics of data systems.
“Meanwhile, STS scholars conducting studies in the public sector have used Kitchin and Lauriault's framework of data assemblages [66] to deeply study the intersection of bureaucratic processes and algorithmic decision-making by examining the politics of data systems [71,101,126].”
From A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed through a Case Study of Child-Welfare (2021) · cites this paper as [101]
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Beyond Moral Coupling: Analysing Politics of Privacy in the Era of Surveillance
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Soberanía digital: un debate abierto y tres problemas políticos
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Children’s access to child protection social work through mobile apps
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Digital technologies in social work: an umbrella review
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Chapter 3 DATAFIED CHILD WELFARE SERVICES AS SITES OF STRUGGLE
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- Authors
- Redden, J., Dencik, L., & Warne, H.
- Affiliations
- Cardiff University (GB)
- Corresponding author
- Redden, Joanna
- Venue
- Policy Studies
- Publisher
- Informa UK Limited
- Published
- 2020-02-14
- Volume
- 41
- Issue
- 5
- Pages
- 507-526
- ISSN
- 0144-2872 (ISSN-L)
- 1470-1006
- OpenAlex
- W3005834607
- Cited by
- 89
- References
- 61
- Citations by year
- 2026: 14 · 2025: 14 · 2024: 11 · 2023: 17 · 2022: 17 · 2021: 10 · 2020: 6
- FWCI
- 17.04
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Policy Studies (publishedVersion, cc-by)
- Goldsmiths (University of London) (acceptedVersion)
- ORCA Online Research @Cardiff (Cardiff University) (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- ORCA Online Research @Cardiff (Cardiff University) (submittedVersion)
- Funders
- The Open Society Foundations
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Safety Research › Child Welfare and Adoption
- Social Sciences › Political Science and International Relations › E-Government and Public Services
- Keywords
- child welfare, data systems, data assemblages, predictive analytics, data sharing, public-private partnerships, power relations, data governance, decision-making, sensitive data, privacy, child protection, England
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