  Group imbalance, resulting from inadequate or unrepresentative data
collection methods, is a primary cause of representation bias in datasets.
Representation bias can exist with respect to different groups of one or more
protected attributes and might lead to prejudicial and discriminatory outcomes
toward certain groups of individuals; in cases where a learning model is
trained on such biased data. This paper presents MASC, a data augmentation
approach that leverages affinity clustering to balance the representation of
non-protected and protected groups of a target dataset by utilizing instances
of the same protected attributes from similar datasets that are categorized in
the same cluster as the target dataset by sharing instances of the protected
attribute. The proposed method involves constructing an affinity matrix by
quantifying distribution discrepancies between dataset pairs and transforming
them into a symmetric pairwise similarity matrix. A non-parametric spectral
clustering is then applied to this affinity matrix, automatically categorizing
the datasets into an optimal number of clusters. We perform a step-by-step
experiment as a demo of our method to show the procedure of the proposed data
augmentation method and evaluate and discuss its performance. A comparison with
other data augmentation methods, both pre- and post-augmentation, is conducted,
along with a model evaluation analysis of each method. Our method can handle
non-binary protected attributes so, in our experiments, bias is measured in a
non-binary protected attribute setup w.r.t. racial groups distribution for two
separate minority groups in comparison with the majority group before and after
debiasing. Empirical results imply that our method of augmenting dataset biases
using real (genuine) data from similar contexts can effectively debias the
target datasets comparably to existing data augmentation strategies.
