Unbiased Subdata Selection for Fair Classification: A Unified Framework and Scalable Algorithms

Understand

As an important problem in modern data analytics, classification has witnessed varieties of applications from different domains.

• Different from conventional classification approaches, fair classification concerns the issues of unintentional biases against the sensitive features (e.g., gender, race).
• Due to high nonconvexity of fairness measures, existing methods are often unable to model exact fairness, which can cause inferior fair classification outcomes.
• This paper fills the gap by developing a novel unified framework to jointly optimize accuracy and fairness.