  Improving the fairness of machine learning models is a nuanced task that
requires decision makers to reason about multiple, conflicting criteria. The
majority of fair machine learning methods transform the error-fairness
trade-off into a single objective problem with a parameter controlling the
relative importance of error versus fairness. We propose instead to directly
optimize the error-fairness tradeoff by using multi-objective optimization. We
present a flexible framework for defining the fair machine learning task as a
weighted classification problem with multiple cost functions. This framework is
agnostic to the underlying prediction model as well as the metrics. We use
multiobjective optimization to define the sample weights used in model training
for a given machine learner, and adapt the weights to optimize multiple metrics
of fairness and accuracy across a set of tasks. To reduce the number of
optimized parameters, and to constrain their complexity with respect to
population subgroups, we propose a novel meta-model approach that learns to map
protected attributes to sample weights, rather than optimizing those weights
directly. On a set of real-world problems, this approach outperforms current
state-of-the-art methods by finding solution sets with preferable
error/fairness trade-offs.
