  When might human input help (or not) when assessing risk in fairness domains?
Dressel and Farid (2018) asked Mechanical Turk workers to evaluate a subset of
defendants in the ProPublica COMPAS data for risk of recidivism, and concluded
that COMPAS predictions were no more accurate or fair than predictions made by
humans. We delve deeper into this claim to explore differences in human and
algorithmic decision making. We construct a Human Risk Score based on the
predictions made by multiple Turk workers, characterize the features that
determine agreement and disagreement between COMPAS and Human Scores, and
construct hybrid Human+Machine models to predict recidivism. Our key finding is
that on this data set, Human and COMPAS decision making differed, but not in
ways that could be leveraged to significantly improve ground-truth prediction.
We present the results of our analyses and suggestions for data collection best
practices to leverage complementary strengths of human and machines in the
fairness domain.
