  In today's society, AI systems are increasingly used to make critical
decisions such as credit scoring and patient triage. However, great convenience
brought by AI systems comes with troubling prevalence of bias against
underrepresented groups. Mitigating bias in AI systems to increase overall
fairness has emerged as an important challenge. Existing studies on mitigating
bias in AI systems focus on eliminating sensitive demographic information
embedded in data. Given the temporal and contextual complexity of
conceptualizing fairness, lossy treatment of demographic information may
contribute to an unnecessary trade-off between accuracy and fairness,
especially when demographic attributes and class labels are correlated. In this
paper, we present an information-lossless de-biasing technique that targets the
scarcity of data in the disadvantaged group. Unlike the existing work, we
demonstrate, both theoretically and empirically, that oversampling
underrepresented groups can not only mitigate algorithmic bias in AI systems
that consistently predict a favorable outcome for a certain group, but improve
overall accuracy by mitigating class imbalance within data that leads to a bias
towards the majority class. We demonstrate the effectiveness of our technique
on real datasets using a variety of fairness metrics.
