Navigating ethical minefields: a multi-stakeholder approach to assessing interconnected risks in generative AI using grey DEMATEL

Abstract

The rapid advancement of generative artificial intelligence (AI) technologies has introduced unprecedented capabilities in content creation and human-AI interaction, while simultaneously raising significant ethical concerns. This study examined the complex landscape of ethical risks associated with generative AI (GAI) through a novel multi-stakeholder empirical analysis using the grey decision-making-trial-and-evaluation-laboratory methodology to quantitatively analyze the causal relationships between risks and their relative influence on AI deployment outcomes. Through a comprehensive literature review and expert validation across three key stakeholder groups (AI developers, end users, and policymakers), we identified and analyzed 14 critical ethical challenges across the input, training, and output modules, including both traditional and emerging risks, such as deepfakes, intellectual property rights, data transparency, and algorithmic bias. This study analyzed the perspectives of key stakeholders to understand how ethical risks are perceived, prioritized, and interconnected in practice. Using Euclidean-distance analysis, we identified significant divergences in risk perception among stakeholders, particularly in areas of adversarial prompts, data bias, and output bias. Our findings contribute to the development of a balanced ethical risk framework by categorizing risks into four distinct zones: critical enablers, mild enablers, independent enablers, and critical dependents. This categorization promotes technological advancement and responsible AI deployment. This study addressed the current gaps in academic work by providing actionable recommendations for risk-mitigation strategies and policy development while highlighting the need for collaborative approaches among stakeholders in the rapidly evolving field of GAI.

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