Ethical concerns of generative AI and mitigation strategies: A systematic mapping study

Abstract

Generative AI technologies, particularly Large Language Models (LLMs), have transformed numerous domains by enhancing convenience and efficiency in information retrieval, content generation, and decision-making processes. However, deploying LLMs also presents diverse ethical challenges, and their mitigation strategies remain complex and domain-dependent. This paper aims to identify and categorise the key ethical concerns associated with using LLMs, examine existing mitigation strategies, and assess the outstanding challenges in implementing these strategies across various domains. We conducted a systematic mapping study, reviewing 39 studies that discuss ethical concerns and mitigation strategies related to LLMs. We analysed these ethical concerns using five ethical dimensions we extracted from various existing guidelines and frameworks, along with an analysis of mitigation strategies and implementation challenges. Our findings reveal that ethical concerns in LLMs are multi-dimensional and context-dependent. While proposed mitigation strategies address some of these concerns, significant challenges still remain. Our results highlight that ethical issues often hinder the practical implementation of mitigation strategies, particularly in high-stakes areas such as healthcare and public governance. Existing frameworks are often inflexible, failing to accommodate evolving societal expectations and diverse contexts.

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