  In the United States and elsewhere, risk assessment algorithms are being used
to help inform criminal justice decision-makers. A common intent is to forecast
an offender's ``future dangerousness.'' Such algorithms have been correctly
criticized for potential unfairness, and there is an active cottage industry
trying to make repairs. In this paper, we use counterfactual reasoning to
consider the prospects for improved fairness when members of a less privileged
group are treated by a risk algorithm as if they are members of a more
privileged group. We combine a machine learning classifier trained in a novel
manner with an optimal transport adjustment for the relevant joint probability
distributions, which together provide a constructive response to claims of
bias-in-bias-out. A key distinction is between fairness claims that are
empirically testable and fairness claims that are not. We then use confusion
tables and conformal prediction sets to evaluate achieved fairness for
projected risk. Our data are a random sample of 300,000 offenders at their
arraignments for a large metropolitan area in the United States during which
decisions to release or detain are made. We show that substantial improvement
in fairness can be achieved consistent with a Pareto improvement for protected
groups.
