Pruss, Dasha (2021) Mechanical Jurisprudence and Domain Distortion: How Predictive Algorithms Warp the Law. [Preprint]

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Abstract

The value-ladenness of computer algorithms is typically framed around issues of epistemic risk. In this paper, 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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Item Type:	Preprint
Creators:	Creators Email ORCID Pruss, Dasha dasha.pruss@pitt.edu
Keywords:	values in science, algorithmic fairness, computer science, philosophy of law, jurisprudence
Subjects:	Specific Sciences > Artificial Intelligence > AI and Ethics General Issues > Technology General Issues > Values In Science
Depositing User:	Dasha Pruss
Date Deposited:	10 Sep 2024 12:55
Last Modified:	10 Sep 2024 12:55
Item ID:	23890
Subjects:	Specific Sciences > Artificial Intelligence > AI and Ethics General Issues > Technology General Issues > Values In Science
Date:	1 January 2021
URI:	https://philsci-archive.pitt.edu/id/eprint/23890

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• Mechanical Jurisprudence and Domain Distortion: How Predictive Algorithms Warp the Law. (deposited 02 Apr 2021 14:21)

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