Qualitative Study on Human-Centered Artificial Intelligence Frameworks for Mitigating Systemic Inequalities in Workforce: A Review

Vollständiger Abstract

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The proliferation of artificial intelligence (AI) and algorithmic architectures within structured human resource environments has raised serious concerns about the digital amplification of systemic bias and intersectional inequalities. This paper presents a systematic literature review of peer-reviewed articles published between 2020 and 2026 focusing on Human-Centered AI (HCAI) frameworks engineered to safeguard and promote workforce equity. Moving beyond standard data-centric mitigation strategies, this review synthesizes scholarship across management sciences, sociology, and computational ethics to analyze how algorithmic inequality regimes are constructed and subsequently dismantled. The review demonstrates that standalone technical bias-correction algorithms fail to mitigate historically embedded institutional discrimination unless coupled with robust human-in-the-loop oversight, ethical compliance auditing, and explicit regulatory accountability. Synthesizing contemporary literature, this study presents an integrated governance framework for organizational practitioners to proactively eliminate systemic demographic barriers while maximizing technological utility within corporate ecosystems.