Ethical Bias Mitigation in Large Language Models: A Comparative Evaluation of Fairness-Aware Fine-Tuning Strategies

Abstract

Large Language Models (LLMs) have rapidly transformed the landscape of natural language processing, enabling remarkable advancements in machine translation, content generation, and human–computer interaction. However, their deployment has raised critical ethical concerns due to the propagation and amplification of societal biases embedded in training data. This work examines how well fairness-aware fine-tuning methods can reduce bias in LLMs without materially impairing model performance. The study examines how well each strategy works across important fairness criteria including Equal Opportunity Difference and Demographic Parity by comparing adversarial debiasing, reweighting, and fairness-regularized loss functions. We refine a mid-sized transformer-based language model and rigorously assess trade-offs between accuracy, computational overhead, and fairness using benchmark datasets with known bias patterns. The findings show that some hybrid approaches provide promise balance, even if no one methodology can attain total bias neutrality. The results support the inclusion of fairness objectives in the first phases of model creation and highlight the significance of context-aware model tweaking. This research contributes to the broader discourse on ethical AI, offering actionable insights for building transparent, accountable, and socially responsible language technologies.