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May 10, 2026Open Access

Bias in AI Admission and Hiring

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Authors

TPTaisiіa Prykhodko

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Overview

Randomized trial examines algorithmic bias in admission and hiring, suggesting a framework for responsible AI governance.

Key Points

  • This paper aims to analyze algorithmic bias in AI systems used for admission and hiring, highlighting the risks of inequality.
  • Examined technical sources of algorithmic bias through a critical synthesis methodology
  • Analyzed the intersection of deep learning architectures and regulatory frameworks like the EU AI Act
  • Proposed a governance framework focusing on Algorithmic Impact Assessments and auditing processes.
  • Identified significant challenges with fairness metrics and the introduction of risks from large language models.
  • Proposed a shift from Human-in-the-Loop to Human-in-Command architectures for better accountability.
  • Concluded the need for AI systems to support human oversight rather than act as gatekeepers.

Cite This Study

Taisiіa Prykhodko (2026) studied this question.

synapsesocial.com/papers/6a002222c8f74e3340f9d0e3https://doi.org/10.5281/zenodo.20079909
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1AI and Bias in Recruitment: Ensuring Fairness in Algorithmic Hiring.2025 · 1 citations
  2. 2Algorithmic Bias in AI Systems: Ethical Risks and Fairness Solutions2026
  3. 3MAKING BIAS VISIBLE: ALGORITHMIC HIRING AND THE ROLE OF GOVERNANCE IN EARLY-STAGE RECRUITMENT2026
  4. 4Fairness in Artificial Intelligence: Understanding and Mitigating Algorithmic Bias2026
  5. 5Ensuring Fairness in Artificial Intelligence: A Study on Algorithmic Bias2026