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

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 sociotechnical tools can lead to disparate predicted risk scores for the same individual. Synthesizing diverse perspectives from machine learning, statistics, sociology, criminology, law, philosophy and economics, we conceptualize 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.