Mechanical Jurisprudence and Domain Distortion: How Predictive Algorithms Warp the Law.

The value-ladenness of computer algorithms is typically framed around issues of epistemic risk. In this article, I examine a deeper sense of value-ladenness: algorithmic methods are not only themselves value-laden but also introduce value into how we reason about their domain of application. I call...

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Published in:Philosophy of Science Vol. 88; no. 5; pp. 1101 - 1113
Main Author: Pruss, Dasha
Format: Article
Published: Cambridge University Press Dec2021
Subjects:
Online Access:View this record in EBSCOhost
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        atl: Mechanical Jurisprudence and Domain Distortion: How Predictive Algorithms Warp the Law.
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        au: Pruss, Dasha
      su:
        Algorithms
        Jurisprudence
        Risk assessment
        Punishment
        Maxima & minima
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        subj:
          Algorithms
          Jurisprudence
          Risk assessment
          Punishment
          Maxima & minima
      ab: The value-ladenness of computer algorithms is typically framed around issues of epistemic risk. In this article, I examine a deeper sense of value-ladenness: algorithmic methods are not only themselves value-laden but also introduce value into how we reason about their domain of application. I call this domain distortion. In particular, using insights from jurisprudence, I show that the use of recidivism risk assessment algorithms (1) presupposes legal formalism and (2) blurs the distinction between liability assessment and sentencing, which distorts how the domain of criminal punishment is conceived and provides a distinctive avenue for values to enter the legal process.
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