id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
ajst-32565	Chen, Lin	Fairness-Aware Classification Based on Rawlsian Veil of Ignorance: A Mathematical Framework for Bias Detection and Mitigation in Machine Learning	2025	14	.pdf	application/pdf	5490	284	40	Previous research in algorithmic fairness has approached the problem from multiple perspectives, establishing various fairness metrics and mitigation strategies that reflect different normative commitments and technical constraints. Research has explored Rawlsian approaches to algorithmic fairness, noting that the theory is commonly applied but that proposals often aim to uphold the difference principle in individual situations[10].	cache/ajst-32565.pdf	txt/ajst-32565.txt