Designing Ethical and Context-Aware AI Evaluation Frameworks for Resume Screening Agents Using Large Language Models

With the surge in edge technology and adoption of technology within companies demand right Talent at right time. Increasing reliance on AI-driven resume screening tools demands the development of ethical and context-aware evaluation frameworks to ensure unbiased and accurate candidate assessments. This paper introduces TalentLens.AI, an AI-powered resume screening and preliminary candidate evaluation bot, a smart and efficient way to identify top talent. TalentLens.AI bot is designed based on research to enhance fairness, transparency, and contextual understanding in hiring processes. We explore the integration of Large Language Models (LLMs) in TalentLens.AI, analyze their comparative performance, and propose a scalable evaluation framework that incorporates ethical AI principles. A benchmarking study of state-of-the-art LLMs (GPT-4, Claude, Mistral, Cohere, and LLaMA 2) is conducted, focusing on bias mitigation, semantic understanding, and computational efficiency. This work contributes to the broader discourse on responsible AI in talent acquisition, with potential applications in HR technology and workforce management.