Achieving Fairness at No Utility Cost via Data Reweighing with Influence. (arXiv:2202.00787v2 [cs.LG] UPDATED)

With the fast development of algorithmic governance, fairness has become a
compulsory property for machine learning models to suppress unintentional
discrimination. In this paper, we focus on the pre-processing aspect for
achieving fairness, and propose a data reweighing approach that only adjusts
the weight for samples in the training phase. Different from most previous
reweighing methods which usually assign a uniform weight for each (sub)group,
we granularly model the influence of each training sample with regard to
fairness-related quantity and …

arxiv cost data fairness influence lg