Mitigating Bias in AI-Powered Recruitment: Techniques, Tools, and Lessons from Real-World Systems

This paper explores the design and deployment of AI-powered recruitment systems with a focus on bias mitigation. It presents real-world architectural patterns, fairness techniques, and evaluation frameworks used in the development of NobleMatch, an AI-enabled talent matching tool. The study highlights challenges in training and deploying large language models (LLMs) and embedding-based candidate matching pipelines, particularly in mitigating gender and socio-economic biases. Techniques such as adversarial debiasing, human-in-the-loop oversight, and statistical fairness metrics are examined through synthetic experiments. This work contributes a practical reference for designing ethically responsible AI hiring systems while maintaining performance, and provides lessons for both startups and enterprises building HR technology solutions.