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Carceral Machines: Algorithmic Risk Assessment and the Reshaping of Crime and Punishment

Pruss, Dasha (2023) Carceral Machines: Algorithmic Risk Assessment and the Reshaping of Crime and Punishment. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Recidivism risk assessment instruments are used in high-stakes pre-trial, sentencing, or parole decisions in nearly every U.S. state. These algorithmic decision-making systems, which estimate a defendant's risk of rearrest or reconviction based on past data, are often presented as an 'evidence-based' strategy for criminal legal reform. In this dissertation, I critically examine how automated decision-making systems like these shape, and are shaped by, social values. I begin with an analysis of algorithmic bias and the limits of technical audits of algorithmic decision-making systems; the subsequent chapters invite readers to consider how social values can be expressed and reinforced by risk assessment instruments in ways that go beyond algorithmic bias. I present novel analyses of the impacts of the Sentence Risk Assessment Instrument in Pennsylvania and cybernetic models of crime in the 1960s Soviet Union. Drawing on methods from history and philosophy of science, sociology, and legal theory, I show not only how societal values about punishment and control shape (and are shaped by) the use of these algorithms – a phenomenon I term domain distortion – but also how the instruments interact with their users – judges – and existing institutional norms around measuring and sentencing crime. My empirical and theoretical findings illustrate the kinds of insidious algorithmic harms that rarely make headlines, and serve as a tonic for the exaggerated and speculative discourse around AI systems in the criminal legal system and beyond.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Pruss, Dashadasha.pruss@pitt.eduddp250000-0002-8124-5266
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee CoChairAllen,
Committee CoChairDanks,
Committee MemberMitchell,
Committee MemberMachery,
Date: 6 September 2023
Date Type: Publication
Defense Date: 22 May 2023
Approval Date: 6 September 2023
Submission Date: 26 May 2023
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 182
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > History and Philosophy of Science
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Risk assessment, algorithm, criminal justice, criminal legal system, values in science, human-AI interaction, algorithm aversion, algorithmic fairness, Soviet cybernetics
Date Deposited: 06 Sep 2023 19:16
Last Modified: 06 Sep 2023 19:16

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