Work

Small language models in big data marketing analytics: Addressing bias, accuracy and ethical challenges

Year: 2025

Type: article

Abstract: Small Language Models (SLMs) are gaining prominence in big data marketing analytics due to their efficiency and scalability. This study evaluates the role of SLMs, emphasizing their benefits in tasks such as sentiment analysis and customer segmentation while addressing limitations, including biases, accuracy constraints, and ethical considerations. Using a mixed-methods approach, the research integrates experimental testing, literature reviews, and expert interviews to compare SLMs with larger models, focusing on performance, bias mitigation, and ethical compliance. The findings underscore the need for strategies to reduce biases, improve transparency, and ensure ethical deployment, enabling SLMs to be leveraged effectively in marketing analytics

Source: International Journal of Science and Research Archive

DOI: 10.30574/ijsra.2025.14.2.0445

Authors: Michael Ehiedu Usiagwu, Lawal Muez, Johnson Chinonso

Institutions: National Open University of Nigeria, University of Salford, Ladoke Akintola University of Technology

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

SDG: Reduced inequalities

Open Access status: hybrid

OpenAlex ID: W4407591891