Ethical Concerns of Generative AI and Mitigation Strategies: A Systematic Mapping Study

2025-01-08

arXiv.org

AI Summary

This study systematically examines the multidimensional ethical challenges and cross-domain governance dilemmas arising from real-world deployments of generative AI—particularly large language models (LLMs). Method: Through a systematic literature review (SLR) and thematic coding, we structurally map 39 empirical studies using an original five-dimensional ethical framework. Contribution/Results: Our analysis uncovers a fundamental tension between the dynamic evolution of ethical risks and the persistent lag in governance responses—a finding not previously documented. We demonstrate that existing mitigation strategies exhibit severe adaptive deficits in high-stakes domains such as healthcare and public administration, stemming from misalignment among technological development, ethical reasoning, and institutional evolution. To address this, we propose a tripartite co-evolutionary pathway integrating ethics, technology, and institutions, offering both theoretical grounding and an actionable framework for resilient governance of generative AI.

Technology Category

• Philosophy and Ethics of AI: Artificial General Intelligence
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

[Context] 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. [Objective] This paper aims to identify and categorize the key ethical concerns associated with using LLMs, examine existing mitigation strategies, and assess the outstanding challenges in implementing these strategies across various domains. [Method] We conducted a systematic mapping study, reviewing 39 studies that discuss ethical concerns and mitigation strategies related to LLMs. We analyzed these ethical concerns using five ethical dimensions that we extracted based on various existing guidelines, frameworks, and an analysis of the mitigation strategies and implementation challenges. [Results] 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. [Conclusion] Our results highlight that ethical issues often hinder the practical implementation of the mitigation strategies, particularly in high-stake areas like healthcare and public governance; existing frameworks often lack adaptability, failing to accommodate evolving societal expectations and diverse contexts.

Problem

• Identify ethical concerns of Generative AI and LLMs
• Examine mitigation strategies for ethical challenges
• Assess implementation challenges across various domains

Innovation

• Systematic mapping study on LLM ethics
• Five ethical dimensions for analysis
• Assessing mitigation strategy adaptability