arXiv:2505.05471
공식 코드 · Papers with Code
Facets of Disparate Impact: Evaluating Legally Consistent Bias in Machine Learning
원문 초록
Leveraging current legal standards, we define bias through the lens of marginal benefits and objective testing with the novel metric "Objective Fairness Index". This index combines the contextual nuances of objective testing with metric stability, providing a legally consistent and reliable measure. Utilizing the Objective Fairness Index, we provide fresh insights into sensitive machine learning applications, such as COMPAS (recidivism prediction), highlighting the metric's practical and theoretical significance. The Objective Fairness Index allows one to differentiate between discriminatory tests and systemic disparities.
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A Reproducibility Study of Product-side Fairness in Bundle Recommendation
Fairness-Aware Grouping for Continuous Sensitive Variables: Application for Debiasing Face Analysis with respect to Skin Tone
Guiding LLM Decision-Making with Fairness Reward Models
Fairness Is Not Enough: Auditing Competence and Intersectional Bias in AI-powered Resume Screening
Datasets for Fairness in Language Models: An In-Depth Survey
hyperFA*IR: A hypergeometric approach to fair rankings with finite candidate pool
토론
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