The rapid expansion of Artificial Intelligence (AI) and Machine Learning (ML) has transformed data-driven decision-making across industries. However, these technologies often amplify existing biases, disproportionately affecting vulnerable groups. This study explores bias detection and mitigation in AI-driven recruitment using Explainable AI (XAI), specifically Local Interpretable Model-Agnostic Explanations (LIME). By analyzing ML recruitment models, we assess accuracy and fairness, ensuring transparency in decision-making. We apply fairness metrics such as equal opportunity and equalized odds to evaluate bias impact. Our approach enhances governance strategies at the design and implementation stages, fostering ethical AI adoption. By promoting fair, interpretable hiring models, this research contributes to building equitable, bias-aware AI applications across industries.