Aleatoric and Epistemic Discrimination: Fundamental Limits of Fairness Interventions

arXiv:2301.11781v3 Announce Type: replace
Abstract: Machine learning (ML) models can underperform on certain population groups due to choices made during model development and bias inherent in the data. We categorize sources of discrimination in the ML pipeline into two classes: aleatoric discrimination, which is inherent in the data distribution, and epistemic discrimination, which is due to decisions made during model development. We quantify aleatoric discrimination by determining the performance limits of a model under fairness constraints, assuming perfect knowledge of …

abstract arxiv bias cs.cy cs.it cs.lg data development discrimination distribution fairness machine machine learning math.it model development pipeline population stat.ml type