Year: 2025
Type: article
Abstract: This study proposes a novel Multi-Agent System (MAS) framework powered by large language models to enhance fairness, transparency, and efficiency in graduate admissions. The MAS architecture features specialized agents—including a Diversity, Equity, and Inclusion Agent and personality-calibrated evaluators—that assess applicants holistically while addressing biases stemming from cognitive distortions, reviewer personality traits, and systemic inconsistencies. It integrates human-AI feedback loops, fairness auditing techniques, and explainable decision-making to promote equitable outcomes. This research advances AI-assisted selection processes by operationalizing fairness principles through agent collaboration and adaptive, auditable decision workflows.
Authors: Sanatizadeh, Aida, Tafti, Ali
Institutions: Northern Illinois University, University of Illinois Chicago
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
SDG: Reduced inequalities
Open Access status: green
OpenAlex ID: W7051566610
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