Bias and Fairness in Large Language Models: Challenges and Mitigation Strategies

Abstract

This paper provides a systematic, expertlevel review of the complex interplay between bias and fairness in Large Language Models (LLMs). As LLMs increasingly permeate critical societal domains, their propensity to learn, perpetuate, and amplify harmful social biases—derived from vast internet training data—presents a fundamental challenge to responsible AI development [1, 2]. This analysis delineates the origins and taxonomy of biases, ranging from overt demographic stereotypes (gender, race) to subtle structural phenomena (position bias) and complex intersectional harms. Crucially, the paper formalizes fairness through mathematical criteria, contrasting group fairness definitions (e.g., Equal Opportunity, Predictive Parity) with individual metrics (Counterfactual Fairness). The core of this analysis surveys state-of-the-art mitigation strategies across the LLM lifecycle: from data preprocessing and in-training regularization to advanced post-processing techniques like Constitutional AI and prompt engineering. Finally, the report explores critical challenges,   including the persistent accuracy-fairness trade-off and legal accountability gaps exacerbated by proprietary model opacity, proposing actionable future research directions guided by frameworks like the NIST AI Risk Management Framework [3, 4]. This synthesis aims to empower researchers and practitioners in developing equitable and trustworthy LLM systems.