Year: 2025
Type: article
Abstract: This article presents a comprehensive framework for real-time bias detection and mitigation in Large Language Models (LLMs) within enterprise environments.The article addresses critical challenges in maintaining fairness while preserving privacy and performance in AI systems.The article introduces novel approaches including a sophisticated statistical evaluation method for bias detection, an adaptive correction system leveraging reinforcement learning, and a scalable implementation architecture.The solution incorporates privacy-preserving mechanisms, dynamic prompt adjustment strategies, and cross-domain policy adaptation techniques.The article employs a multifaceted approach to bias mitigation, combining real-time monitoring, secure data handling, and ethical AI principles.Through extensive empirical evaluation across various domains including hiring systems, customer service, and marketing content generation, it demonstrates the framework's effectiveness in reducing demographic biases while maintaining high task performance.The article contributes significant advancements in practical bias mitigation strategies for enterprise-scale AI applications, offering a balanced approach to fairness, privacy, and performance optimization.
Author: Shekhar Agrawal
Institution: University of Cincinnati
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
SDG: Reduced inequalities
Open Access status: bronze
OpenAlex ID: W4407528595