The DOI matches its Crossref record.
Against Prediction: Profiling, Policing, and Punishing in an Actuarial Age
Overview
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Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Third, and most importantly, most studies fail to analyze contexts of reception, implicitly assuming -usually without empirical data -that police officers, judges, and prosecutors rely uncritically on what algorithms direct them to do in their daily routines (Harcourt 2006;Hvistendahl 2016;Mohler et al. 2015;Uchida and Swatt 2013).”
From Technologies of Crime Prediction: The Reception of Algorithms in Policing and Criminal Courts (2021) · cites this paper as Harcourt 2006
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 Harcourt 2006
In the citing paper
“Both predictive policing and risk-assessment algorithms have faced growing criticism because of their potential to reinforce preexisting biases and inequalities.”
From Technologies of Crime Prediction: The Reception of Algorithms in Policing and Criminal Courts (2021) · cites this paper as Harcourt 2006
In the citing paper
“Scholars have shown that, because algorithms learn by being fed historical data, inequalities from the past will be reflected into the future (Angwin et al. 2016;Brayne 2017;Harcourt 2006;Mayson 2019).”
From Technologies of Crime Prediction: The Reception of Algorithms in Policing and Criminal Courts (2021) · cites this paper as Harcourt 2006
Citations
Cited by
Sorted by citations
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Kellogg, K. C., Valentine, M. A., & Christin, A. (2020) · Academy of Management Annals · doi:10.5465/annals.2018.0174 · Cited by 2,732
Fairness in Criminal Justice Risk Assessments: The State of the Art
Berk, R., Heidari, H., Jabbari, S., Kearns, M., & Roth, A. (2021) · Sociological Methods & Research · doi:10.1177/0049124118782533 · Cited by 988
Where fairness fails: data, algorithms, and the limits of antidiscrimination discourse
Hoffmann, A. L. (2019) · Information, Communication & Society · doi:10.1080/1369118x.2019.1573912 · Cited by 535
Veale, M., & Binns, R. (2017) · Big Data & Society · doi:10.1177/2053951717743530 · Cited by 512
Algorithms in practice: Comparing web journalism and criminal justice
Christin, A. (2017) · Big Data & Society · doi:10.1177/2053951717718855 · Cited by 379
Metadata
- Authors
- Harcourt, B. E.
- Publisher
- University of Chicago Press
- Published
- 2006
- Cited by
- 193
- References
- 0
- Language
- en
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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