  Data-driven algorithms are only as good as the data they work with, while
data sets, especially social data, often fail to represent minorities
adequately. Representation Bias in data can happen due to various reasons
ranging from historical discrimination to selection and sampling biases in the
data acquisition and preparation methods. Given that "bias in, bias out", one
cannot expect AI-based solutions to have equitable outcomes for societal
applications, without addressing issues such as representation bias. While
there has been extensive study of fairness in machine learning models,
including several review papers, bias in the data has been less studied. This
paper reviews the literature on identifying and resolving representation bias
as a feature of a data set, independent of how consumed later. The scope of
this survey is bounded to structured (tabular) and unstructured (e.g., image,
text, graph) data. It presents taxonomies to categorize the studied techniques
based on multiple design dimensions and provides a side-by-side comparison of
their properties. There is still a long way to fully address representation
bias issues in data. The authors hope that this survey motivates researchers to
approach these challenges in the future by observing existing work within their
respective domains.
