  This paper explores how different ideas of racial equity in machine learning,
in justice settings in particular, can present trade-offs that are difficult to
solve computationally. Machine learning is often used in justice settings to
create risk assessments, which are used to determine interventions, resources,
and punitive actions. Overall aspects and performance of these machine
learning-based tools, such as distributions of scores, outcome rates by levels,
and the frequency of false positives and true positives, can be problematic
when examined by racial group. Models that produce different distributions of
scores or produce a different relationship between level and outcome are
problematic when those scores and levels are directly linked to the restriction
of individual liberty and to the broader context of racial inequity. While
computation can help highlight these aspects, data and computation are unlikely
to solve them. This paper explores where values and mission might have to fill
the spaces computation leaves.
