  Fairness is a critical trait in decision making. As machine-learning models
are increasingly being used in sensitive application domains (e.g. education
and employment) for decision making, it is crucial that the decisions computed
by such models are free of unintended bias. But how can we automatically
validate the fairness of arbitrary machine-learning models? For a given
machine-learning model and a set of sensitive input parameters, our AEQUITAS
approach automatically discovers discriminatory inputs that highlight fairness
violation. At the core of AEQUITAS are three novel strategies to employ
probabilistic search over the input space with the objective of uncovering
fairness violation. Our AEQUITAS approach leverages inherent robustness
property in common machine-learning models to design and implement scalable
test generation methodologies. An appealing feature of our generated test
inputs is that they can be systematically added to the training set of the
underlying model and improve its fairness. To this end, we design a fully
automated module that guarantees to improve the fairness of the underlying
model.
  We implemented AEQUITAS and we have evaluated it on six state-of-the-art
classifiers, including a classifier that was designed with fairness
constraints. We show that AEQUITAS effectively generates inputs to uncover
fairness violation in all the subject classifiers and systematically improves
the fairness of the respective models using the generated test inputs. In our
evaluation, AEQUITAS generates up to 70% discriminatory inputs (w.r.t. the
total number of inputs generated) and leverages these inputs to improve the
fairness up to 94%.
