Ethical AI prompt recommendations in large language models using collaborative filtering

Abstract

As large language models (LLMs) shape AI development, ensuring ethical prompt recommendations is crucial. LLMs offer innovation but risk bias, fairness issues, and accountability concerns. Traditional oversight methods struggle with scalability, necessitating dynamic solutions. This paper proposes using collaborative filtering, a technique from recommendation systems, to enhance ethical prompt selection. By leveraging user interactions, it promotes ethical guidelines while reducing bias. Contributions include a synthetic dataset for prompt recommendations and the application of collaborative filtering. The work also tackles challenges in ethical AI, such as bias mitigation, transparency, and preventing unethical prompt engineering.

This paper has been accepted to by the International Journal of Parallel, Emergent & Distributed Systems (Taylor and Francis) and has an assigned DOI. We have already chose to make this open access using CC BY. The article is not yet available online on the publisher's website. The DOI is: doi.org/10.1080/17445760.2025.2573086

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