Enhancing Ethical and Fair AI Solutions through Bias-Mitigation Strategies in Large Language Models
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Research Paper (postgraduate) from the year 2025 in the subject Speech Science / Linguistics, grade: 5.5/6, University of Applied Sciences Northwestern Switzerland, course: Generative AI and Large Language Models, language: English, abstract: This study examines how businesses can implement bias-mitigation strategies in Large Language Models (LLMs) to ensure their AI solutions are ethical, fair, and trustworthy. Beginning with a comprehensive review of existing literature, the study identifies current methods and challenges in reducing bias within LLMs. To gain practical perspectives, in-perso...
Research Paper (postgraduate) from the year 2025 in the subject Speech Science / Linguistics, grade: 5.5/6, University of Applied Sciences Northwestern Switzerland, course: Generative AI and Large Language Models, language: English, abstract: This study examines how businesses can implement bias-mitigation strategies in Large Language Models (LLMs) to ensure their AI solutions are ethical, fair, and trustworthy. Beginning with a comprehensive review of existing literature, the study identifies current methods and challenges in reducing bias within LLMs. To gain practical perspectives, in-person discussions with students were conducted in a workshop setting. The findings emphasize the importance of diverse data sets, continuous monitoring, and inclusive development teams in effectively addressing bias. Additionally, the need for businesses to balance ethical considerations with practical implementation is highlighted. By combining theoretical insights with practical input, the study provides actionable recommendations for businesses to develop AI solutions that uphold high ethical standards and align with societal values. The goal is to promote greater transparency, accountability, and trust in AI-driven innovations in the business sector.
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