MEDICAL STUDENT REVERSE-ENGINEERS AI HIRING TOOL

Using Python, the student attempted to understand how artificial intelligence tools used by medical colleges evaluated applicants. His effort stemmed from frustration over repeated rejections, prompting him to investigate whether the algorithms were systematically blocking his candidacy. The reverse-engineering project highlights a growing problem in higher education and hiring: opaque AI systems making consequential decisions about people's careers with little transparency or accountability. Medical schools increasingly rely on automated screening tools to process large application volumes. These systems often operate as black boxes, making it difficult for rejected candidates to understand why they were filtered out or to challenge biased outcomes. The student's technical investigation underscores a critical gap between how AI hiring tools are deployed and how little scrutiny they face. As institutions scale algorithmic screening, questions about fairness, accuracy, and discrimination become harder to ignore. The case illustrates why explainability and auditability matter in recruitment technology—particularly in fields like medicine where selection processes have significant consequences.