Toward Fair NLP Models: Bias Detection and Mitigation in Cloud-Based Text Mining Services
Year: 2024
Type: article
Abstract: As Natural Language Processing (NLP) increasingly becomes essential across various applications, the challenge of bias within these models has attracted considerable scrutiny. Cloud-based text mining services offered by platforms such as Google Cloud, AWS, and Microsoft Azure have made NLP technologies more accessible, allowing businesses and developers to utilize advanced language models. Nevertheless, the existence of biases—be they gender, racial, or socioeconomic—in these models raises significant concerns regarding fairness and equity in automated decision-making processes. This paper examines the pressing issue of bias in cloud-based NLP models, investigating methods for both identifying and alleviating such biases. We analyze current approaches to bias detection, which include dataset evaluation, fairness metrics, and algorithmic audits, and we review techniques for bias mitigation at various stages of the NLP pipeline, from data preprocessing to the post-processing of model outputs. Particular emphasis is placed on the challenges presented by the opaque nature of cloud services, which can obscure model behaviour and impede transparency. The paper concludes with suggestions for incorporating bias mitigation strategies into cloud-based NLP systems to enhance fairness, uphold ethical standards, and ensure responsible AI practices.
Authors: Devashish Bornare -, Mandar Zade -, Shivpratap Jadhav -, Rohit Mohite -, Anuja Chincholkar -
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
SDG: Reduced inequalities
Open Access status: hybrid
OpenAlex ID: W4404791070