Beyond Traditional Biases in AI Hiring: Exposing the Hidden Systemic Challenges in Resume Screening

From automatically curating your music playlist to guiding life-changing hiring decisions, artificial intelligence now discreetly shapes every corner of our world, including how we recruit. In theory, using AI in recruitment streamlines processes and saves valuable time for hiring teams. Yet, concerns remain about whether automated methods are truly fair and equitable. AI can inadvertently encode biases tied to demographic factors such as race and gender, prompting organizations to explore Explainable AI for greater transparency. However, hidden biases may persist undetected, even when personal information is removed. These details can threaten the integrity of our hiring practices and often slip beneath our awareness. Our work explores these overlooked biases, offering insights and strategies to foster more ethical, inclusive recruitment processes. In this paper, we conduct a controlled experiment using a Large Language Modelsbased recruitment tool to quantify how subtle resume variations can influence candidate evaluations. By isolating factors such as “career gaps” and “keyword usage”, we highlight overlooked biases that can significantly alter AIdriven hiring outcomes. Our work explores these overlooked biases, offering insights and strategies to foster more ethical, inclusive recruitment processes. Keywords- Algorithmic bias, AI fairness, LLM-based hiring, Explainable AI, Ethical AI in recruitment