Journal of Human Resource Sustainability and Organizational Studies(JHRSOS)

Ph. D. of Tel Aviv University, The Lester and Sally Antin Faculty of Humanities, School of Philosophy, Linguistics and Science Studies, Israel

Publications

• Research Article   
  From Statistical Fairness to Epistemic Fairness: The DBSD Framework for AI Bias Mitigation
  Author(s): Yair Oppenheim*
  
  Artificial Intelligence (AI), Machine Learning (ML), Large Language Models (LLMs), and data-driven decision systems increasingly influence critical domains including hiring, lending, healthcare, insurance, criminal justice, and digital governance. However, contemporary AI systems frequently reproduce or amplify social inequalities through hidden semantic inference processes that operate beyond explicit discriminatory rules or observable prediction outputs. This article introduces the Deep Bias Systematic Deviation (DBSD) framework, a novel semantic-inferential model that reconceptualizes algorithmic bias as an asymmetric inference-flow phenomenon operating across semantic, behavioral, and networked information structures. Unlike conventional fairness approaches that treat bias primarily as unequal outputs, DBSD models bias as a function of knowledge pressure, semantic alignment, stere.. Read More»
  

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