  Given the magnitude of data generation currently, both in quantity and speed,
the use of machine learning is increasingly important. When data include
protected features that might give rise to discrimination, special care must be
taken. Data quality is critical in these cases, as biases in training data can
be reflected in classification models. This has devastating consequences and
fails to comply with current regulations. Data-Centric Artificial Intelligence
proposes dataset modifications to improve its quality. Instance selection via
undersampling can foster balanced learning of classes and protected feature
values in the classifier. When such undersampling is done close to the decision
boundary, the effect on the classifier would be bolstered. This work proposes
Fair Overlap Number of Balls (Fair-ONB), an undersampling method that harnesses
the data morphology of the different data groups (obtained from the combination
of classes and protected feature values) to perform guided undersampling in the
areas where they overlap. It employs attributes of the ball coverage of the
groups, such as the radius, number of covered instances and density, to select
the most suitable areas for undersampling and reduce bias. Results show that
the Fair-ONB method reduces bias with low impact on the classifier's predictive
performance.
