Forks Over Knives: Predictive Inconsistency in Criminal Justice Algorithmic Risk Assessment Tools

Autor: Travis Greene, Galit Shmueli, Jan Fell, Ching-Fu Lin, Han-Wei Liu
Rok vydání: 2022
Předmět:
Zdroj: Journal of the Royal Statistical Society Series A: Statistics in Society. 185:S692-S723
ISSN: 1467-985X
0964-1998
DOI: 10.1111/rssa.12966
Popis: Big data and algorithmic risk prediction tools promise to improve criminal justice systems by reducing human biases and inconsistencies in decision-making. Yet different, equally justifiable choices when developing, testing and deploying these socio-technical tools can lead to disparate predicted risk scores for the same individual. Synthesising diverse perspectives from machine learning, statistics, sociology, criminology, law, philosophy and economics, we conceptualise this phenomenon as predictive inconsistency. We describe sources of predictive inconsistency at different stages of algorithmic risk assessment tool development and deployment and consider how future technological developments may amplify predictive inconsistency. We argue, however, that in a diverse and pluralistic society we should not expect to completely eliminate predictive inconsistency. Instead, to bolster the legal, political and scientific legitimacy of algorithmic risk prediction tools, we propose identifying and documenting relevant and reasonable ‘forking paths’ to enable quantifiable, reproducible multiverse and specification curve analyses of predictive inconsistency at the individual level.
Databáze: OpenAIRE
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