Towards A Holistic View of Bias in Machine Learning: Bridging Algorithmic Fairness and Imbalanced Learning

Understand

Machine learning (ML) is playing an increasingly important role in rendering decisions that affect a broad range of groups in society.

• ML models inform decisions in criminal justice, the extension of credit in banking, and the hiring practices of corporations.
• This posits the requirement of model fairness, which holds that automated decisions should be equitable with respect to protected features (e.g., gender, race, or age) that are often under-represented in the data.
• We postulate that this problem of under-representation has a corollary to the problem of imbalanced data learning.