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This systematic literature review synthesizes published sources from the ASIS&T Digital Library and the ACM Digital Library to develop a definition of the carceral state and to show how the term has been used in contemporary technology‐focused research. The carceral state concept has been adopted and applied widely in multiple areas of social scientific research to refer to the formal institutions of the criminal justice system proper and other social arrangements, ideologies, practices, and technologies that punish, surveil, and contain populations. Our review reveals a recent and increasing engagement with the carceral state in the collections surveyed. Encouraged by this increasing attention, this review is an attempt to introduce the carceral state as a guiding framework for tech‐society research and to consider implications for advancing responsibility, reflexivity, and care in the creation and evaluation of information systems, programs, and services.
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This content will become publicly available on January 1, 2026
Data technologies and analytics for policy and governance: a landscape review
Abstract Data for Policy (dataforpolicy.org), a trans-disciplinary community of research and practice, has emerged around the application and evaluation of data technologies and analytics for policy and governance. Research in this area has involved cross-sector collaborations, but the areas of emphasis have previously been unclear. Within the Data for Policy framework of six focus areas, this report offers a landscape review of Focus Area 2: Technologies and Analytics. Taking stock of recent advancements and challenges can help shape research priorities for this community. We highlight four commonly used technologies for prediction and inference that leverage datasets from the digital environment: machine learning (ML) and artificial intelligence systems, the internet-of-things, digital twins, and distributed ledger systems. We review innovations in research evaluation and discuss future directions for policy decision-making.
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- Award ID(s):
- 1945332
- PAR ID:
- 10578045
- Publisher / Repository:
- Data and Policy Journal (Cambridge University Press)
- Date Published:
- Journal Name:
- Data & Policy
- Volume:
- 7
- ISSN:
- 2632-3249
- Subject(s) / Keyword(s):
- data science analytics public policy governance
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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