March 29, 2024, 4:42 a.m. | Md Rahat Shahriar Zawad, Peter Washington

cs.LG updates on

arXiv:2403.19165v1 Announce Type: new
Abstract: With the universal adoption of machine learning in healthcare, the potential for the automation of societal biases to further exacerbate health disparities poses a significant risk. We explore algorithmic fairness from the perspective of feature selection. Traditional feature selection methods identify features for better decision making by removing resource-intensive, correlated, or non-relevant features but overlook how these factors may differ across subgroups. To counter these issues, we evaluate a fair feature selection method that considers …

abstract adoption algorithmic fairness arxiv automation biases cs.lg decision decision making explore fair fairness feature features feature selection health healthcare identify machine machine learning making perspective risk type universal

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