Bias Mitigation in NLP: Automated Detection and Correction

DOI: https://doi.org/10.63345/ijrmeet.org.v13.i5.130503

Manish Tripathi

Cornell University

Ithaca, New York, USA

mt737@cornell.edu

Er. Raghav Agarwal

Assistant System Engineer

TCS, Bengaluru

raghavagarwal4998@gmail.com

Abstract – 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.

Keywords – Bias mitigation, NLP systems, automated detection, fairness metrics, sensitivity analysis, adversarial testing, bias correction, data augmentation, algorithmic adjustments, post-processing, fairness and accuracy, ethical guidelines, model development.

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