Discrimination and Class Imbalance Aware Online Naive Bayes. (arXiv:2211.04812v1 [cs.LG])

Fairness-aware mining of massive data streams is a growing and challenging
concern in the contemporary domain of machine learning. Many stream learning
algorithms are used to replace humans at critical decision-making points e.g.,
hiring staff, assessing credit risk, etc. This calls for handling massive
incoming information with minimum response delay while ensuring fair and high
quality decisions. Recent discrimination-aware learning methods are optimized
based on overall accuracy. However, the overall accuracy is biased in favor of
the majority class; therefore, …

arxiv bayes discrimination