  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.
