Bias Mitigation in NLP: Automated Detection and Correction
Manish Tripathi, Raghav Agarwal · International Journal of Research in Modern Engineering & Emerging Technology · 2025
Natural Language Processing (NLP) systems have shown remarkable capabilities, but they often inherit biases from the datasets they are trained on, resulting in outcomes that can be unfair or even harmful. These biases can appear in different forms, such as those related to gender, race, or socioeconomic status. Addressing and mitigating bias in NLP has become a critical area of research, aiming to ensure that machine learning models generate fair and impartial results. This paper delves into the automation of bias detection and correction within NLP systems. It reviews current methods for identifying biases, including fairness metrics, sensitivity analyses, and adversarial testing. Additionally, it examines techniques for mitigating bias, such as data augmentation, algorithmic adjustments, and post-processing methods. The paper also discusses the limitations and challenges of these approaches, emphasizing the balance between maintaining accuracy and promoting fairness. Lastly, it explores potential research directions, such as embedding ethical considerations into model development and establishing more comprehensive frameworks for continuous bias detection and mitigation.