The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
Algorithmic risk governance: Big data analytics, race and information activism in criminal justice debates
Abstract
Meanings of risk in criminal justice assessment continue to evolve, making it critical to understand how particular compositions of risk are mediated, resisted and re-configured by experts and practitioners. Criminal justice organizations are working with computer scientists, software engineers and private companies that are skilled in big data analytics to produce new ways of thinking about and managing risk. Little is known, however, about how criminal justice systems, social justice organizations and individuals are shaping, challenging and redefining conventional actuarial risk episteme(s) through the use of big data technologies. The use of such analytics is shifting organizational risk practices, challenging social science methods of assessing risk, producing new knowledge about risk and consequently new forms of algorithmic governance. This article explores how big data reconfigure risk by producing a new form of algorithmic risk—a form of risk which is posited as different from the social science (psychologically) informed risk techniques already in use in many justice sectors. It also shows that new experts are entering the risk game, including technologists who make data public and accessible to a range of stakeholders. Finally, it demonstrates that big data analytics can be used to produce forms of usable knowledge that constitute types of ‘information activism’. This form of activism produces alternative risk narratives, which are focused on ‘criminogenic structures’ or ‘criminogenic policy’.
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In the citing paper
“Likewise, criminal courts use multiple predictive instruments, called”
From Technologies of Crime Prediction: The Reception of Algorithms in Policing and Criminal Courts (2021) · cites this paper as Hannah-Moffat 2018
In the citing paper
“These issues, nevertheless, are usually ignored with the result that the concept of 'risk' is routinely used to produce and justify a range of governmental approaches to dealing with Indigenous offenders (Hannah-Moffat, 2019).”
From Colonization, Normalization and Risk-Assessment: The Case of Youth Criminal Justice Act and Correctional Overrepresentation of Indigenous Youth in Canada (2025) · cites this paper as Hannah-Moffat, 2019
In the citing paper
“Overwhelmingly, the COMPAS dataset collected in Broward County [58] is used as evidence amongst our sources to suggest that ARA tools do or can exhibit biased and unfair performance when used in the real world.”
From Efficiency vs. Equity: A Structured Interdisciplinary Review of AI in Criminal Justice Risk Assessments (2026) · cites this paper as [58]
In the citing paper
“Several products currently on the market purport to indicate neighborhoods that are more likely to experience crime [58].”
From Efficiency vs. Equity: A Structured Interdisciplinary Review of AI in Criminal Justice Risk Assessments (2026) · cites this paper as [58]
In the citing paper
“More importantly, a large dataset of criminal defendants from 2013 to 2014 in Broward County, which was using COMPAS, was compiled using public record requests [58].”
From Efficiency vs. Equity: A Structured Interdisciplinary Review of AI in Criminal Justice Risk Assessments (2026) · cites this paper as [58]
In the citing paper
“These analyses uncovered that COMPAS recommended 24% longer sentences for young defendants [35] and that COMPAS was more than twice as likely to incorrectly identify a black defendant as a future criminal as compared to a white defendant [58] although Northpoint, the company that developed COMPAS, has disputed claims of bias in COMPAS [58].”
From Efficiency vs. Equity: A Structured Interdisciplinary Review of AI in Criminal Justice Risk Assessments (2026) · cites this paper as [58]
In the citing paper
“These analyses uncovered that COMPAS recommended 24% longer sentences for young defendants [35] and that COMPAS was more than twice as likely to incorrectly identify a black defendant as a future criminal as compared to a white defendant [58] although Northpoint, the company that developed COMPAS, has disputed claims of bias in COMPAS [58].”
From Efficiency vs. Equity: A Structured Interdisciplinary Review of AI in Criminal Justice Risk Assessments (2026) · cites this paper as [58]
Citations
Cites
boyd, D., & Crawford, K. (2012) · Information, Communication & Society · doi:10.1080/1369118x.2012.678878 · Cited by 5,479
Big data: a revolution that will transform how we live, work, and think
(2013) · Choice Reviews Online · doi:10.5860/choice.50-6804 · Cited by 3,657
Hacking, I. (1990) · Cambridge University Press eBooks · doi:10.1017/cbo9780511819766 · Cited by 3,585
Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., et al. (2009) · Science · doi:10.1126/science.1167742 · Cited by 3,365
Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor
Lebovits, H. (2018) · Public Integrity · doi:10.1080/10999922.2018.1511671 · Cited by 3,320
THE LOGIC OF CONNECTIVE ACTION: Digital media and the personalization of contentious politics
Bennett, W. L., & Segerberg, A. (2012) · Information, Communication & Society · doi:10.1080/1369118x.2012.670661 · Cited by 3,116
Fair prediction with disparate impact: a study of bias in recidivism prediction instruments
Chouldechova, A. (2017) · Big Data · doi:10.1089/big.2016.0047 · Cited by 2,159
The New Penology: Notes on the Emerging Strategy of Corrections and Its Implications
Feeley, M., & Simon, J. (1992) · Criminology · doi:10.1111/j.1745-9125.1992.tb01112.x · Cited by 2,109
The British Journal of Criminology
Bennett, T. H. (2017) · Routledge eBooks · doi:10.4324/9781315086613-24 · Cited by 1,970
Networks of outrage and hope: social movements in the internet age
Hands, J. (2013) · Information Communication & Society · doi:10.1080/1369118x.2013.829514 · Cited by 1,897
Research works printed but not linked: 5
- Gitlin T, 2012, Occupy Nation: The Roots, the Spirit, and the Promise of Occupy Wall Street
- 10.1177/0959353504042178 · DOI
- Harcourt BE, 2007, Against Prediction
- Lohr S, 2013, New York Times, F1
- Lupton D, 2015, Digital Sociology
Cited by
Sorted by citations
Algorithms at Work: The New Contested Terrain of Control
Kellogg, K. C., Valentine, M. A., & Christin, A. (2020) · Academy of Management Annals · doi:10.5465/annals.2018.0174 · Cited by 2,732
Predict and Surveil: Data, Discretion, and the Future of Policing
Brayne, S. (2020) · doi:10.1093/oso/9780190684099.001.0001 · Cited by 334
Technologies of Crime Prediction: The Reception of Algorithms in Policing and Criminal Courts
Brayne, S., & Christin, A. (2021) · Social Problems · doi:10.1093/socpro/spaa004 · Cited by 279
Algorithmic justice: Algorithms and big data in criminal justice settings
Završník, A. (2019) · European Journal of Criminology · doi:10.1177/1477370819876762 · Cited by 293
Artificial intelligence, bureaucratic form, and discretion in public service
Bullock, J., Young, M. M., & Wang, Y.-F. (2020) · Information Polity · doi:10.3233/ip-200223 · Cited by 139
Metadata
- Authors
- Hannah-Moffat, K.
- Affiliations
- University of Toronto, Ontario, Canada
- Venue
- Theoretical Criminology
- Publisher
- SAGE Publications
- Published
- 2018-03-22
- Volume
- 23
- Issue
- 4
- Pages
- 453-470
- ISSN
- 1362-4806 (ISSN-L)
- 1461-7439
- OpenAlex
- W2790702402
- Cited by
- 236
- References
- 48
- Citations by year
- 2026: 17 · 2025: 29 · 2024: 27 · 2023: 22 · 2022: 70 · 2021: 20 · 2020: 36 · 2019: 10 · 2018: 5
- FWCI
- 35.29
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Theoretical Criminology (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Sociology and Political Science › Criminal Justice and Corrections Analysis
- Social Sciences › Law › Artificial Intelligence in Law
- Keywords
- big data analytics, information activism, race, risk narratives, social justice organizations, criminal justice organizations
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