  Prediction systems are successfully deployed in applications ranging from
disease diagnosis, to predicting credit worthiness, to image recognition. Even
when the overall accuracy is high, these systems may exhibit systematic biases
that harm specific subpopulations; such biases may arise inadvertently due to
underrepresentation in the data used to train a machine-learning model, or as
the result of intentional malicious discrimination. We develop a rigorous
framework of *multiaccuracy* auditing and post-processing to ensure accurate
predictions across *identifiable subgroups*.
  Our algorithm, MULTIACCURACY-BOOST, works in any setting where we have
black-box access to a predictor and a relatively small set of labeled data for
auditing; importantly, this black-box framework allows for improved fairness
and accountability of predictions, even when the predictor is minimally
transparent. We prove that MULTIACCURACY-BOOST converges efficiently and show
that if the initial model is accurate on an identifiable subgroup, then the
post-processed model will be also. We experimentally demonstrate the
effectiveness of the approach to improve the accuracy among minority subgroups
in diverse applications (image classification, finance, population health).
Interestingly, MULTIACCURACY-BOOST can improve subpopulation accuracy (e.g. for
"black women") even when the sensitive features (e.g. "race", "gender") are not
given to the algorithm explicitly.
