Pruss, Dasha (2021) Mechanical Jurisprudence and Domain Distortion: How Predictive Algorithms Warp the Law. [Preprint]
This is the latest version of this item.
Text
pruss2021_philsciarchive.pdf Download (148kB) |
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.
Export/Citation: | EndNote | BibTeX | Dublin Core | ASCII/Text Citation (Chicago) | HTML Citation | OpenURL |
Social Networking: |
Item Type: | Preprint | ||||||
---|---|---|---|---|---|---|---|
Creators: |
|
||||||
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 |
Available Versions of this Item
Monthly Views for the past 3 years
Monthly Downloads for the past 3 years
Plum Analytics
Actions (login required)
View Item |