  In criminal justice risk forecasting, one can prove that it is impossible to
optimize accuracy and fairness at the same time. One can also prove that it is
impossible optimize at once all of the usual group definitions of fairness. In
the policy arena, one is left with tradeoffs about which many stakeholders will
adamantly disagree. In this paper, we offer a different approach. We do not
seek perfectly accurate and fair risk assessments. We seek politically
acceptable risk assessments. We describe and apply to data on 300,000 offenders
a machine learning approach that responds to many of the most visible charges
of "racial bias." Regardless of whether such claims are true, we adjust our
procedures to compensate. We begin by training the algorithm on White offenders
only and computing risk with test data separately for White offenders and Black
offenders. Thus, the fitted algorithm structure is exactly the same for both
groups; the algorithm treats all offenders as if they are White. But because
White and Black offenders can bring different predictors distributions to the
white-trained algorithm, we provide additional adjustments if needed. Insofar
are conventional machine learning procedures do not produce accuracy and
fairness that some stakeholders require, it is possible to alter conventional
practice to respond explicitly to many salient stakeholder claims even if they
are unsupported by the facts. The results can be a politically acceptable risk
assessment tools.
