Compounding Injustice: History and Prediction in Carceral Decision-Making

TL;DR

This paper investigates how algorithmic risk assessments in criminal justice can reinforce systemic disadvantages through feedback loops, highlighting the influence of incarceration on future criminality and questioning the objectivity of current predictive practices.

Contribution

It provides empirical evidence of criminogenic effects of incarceration and discusses the theoretical implications of feedback effects in repeated carceral decisions.

Findings

Incarceration increases future criminal risk even after controlling for other factors.

Risk assessments are influenced by geographical and demographic biases.

Criminal treatment impacts subsequent criminal convictions.

Abstract

Risk assessment algorithms in criminal justice put people's lives at the discretion of a simple statistical tool. This thesis explores how algorithmic decision-making in criminal policy can exhibit feedback effects, where disadvantage accumulates among those deemed 'high risk' by the state. Evidence from Philadelphia suggests that risk - and, by extension, criminality - is not fundamental or in any way exogenous to political decision-making. A close look at the geographical and demographic properties of risk calls into question the current practice of prediction in criminal policy. Using court docket summaries from Philadelphia, we find evidence of a criminogenic effect of incarceration, even controlling for existing determinants of 'criminal risk'. With evidence that criminal treatment can influence future criminal convictions, we explore the theoretical implications of compounding effects in repeated carceral decisions.