Privatizing Sentencing: A Delegation Framework for Recidivism Risk Assessment

40 Pages Posted: 10 Mar 2019 Last revised: 12 Aug 2020

See all articles by Andrea Nishi

Andrea Nishi

affiliation not provided to SSRN

Date Written: January 22, 2019

Abstract

This paper explores the use of privately developed risk assessment algorithms in criminal sentencing, arguing that these tools are developed in a way that hinders the enforcement of constitutional protections and gives private algorithm developers undue influence in sentencing determinations. Using the private delegation doctrine, which limits Congress's ability to delegate to private actors, the paper aims to strengthen the state statutory frameworks that govern the use of these tools to restore accountability to the sentencing process.

Keywords: risk assessment, sentencing, recidivism, machine learning, private delegation, delegation, State v. Loomis

Suggested Citation

Nishi, Andrea, Privatizing Sentencing: A Delegation Framework for Recidivism Risk Assessment (January 22, 2019). Columbia Law Review, Vol. 119, No. 6, 2019, Available at SSRN: https://ssrn.com/abstract=3335946 or http://dx.doi.org/10.2139/ssrn.3335946

Andrea Nishi (Contact Author)

affiliation not provided to SSRN

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