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

Philosophy of Science 88 (5):1101-1112 (2021)
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Abstract

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 presupposes legal formalism and 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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References found in this work

Complacency and Bias in Human Use of Automation: An Attentional Integration.Raja Parasuraman & Dietrich H. Manzey - 2010 - Human Factors: The Journal of the Human Factors and Ergonomics Society 52 (3):381-410.

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