The Ethics of Generative AI in Social-Scientific Research: A Qualitative Approach for Community-Based AI Ethics

Jeon, June / Park, Jaehyuk / Kim, Lanu

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Despite growing attention to the ethics of generative AI, there has been little discussion about how
research ethics should be updated for the social sciences. This paper fills this gap at the intersection of AI ethics and social-science research ethics. Based on 17 semi-structured interviews, we present three narratives about generative AI and research ethics: 1) the equalizer narrative, 2) the meritocracy narrative, and 3) the community narrative. We argue that the ethics of AI-assisted social-scientific research cannot be reduced to universal checklists. Instead, the community-based approach is necessary to organize “ethics in practice.” In all narratives, unethical practices are identified in the contexts of the technical functions of generative AI and the institutional arrangements of academia. Our findings suggest that democratic deliberation about AI ethics within academic units, such as departments or conferences, is necessary. We end our paper with a discussion guideline for academic communities’ town-hall meetings, which will create opportunities for enhanced reflexivity among social scientists.

Issue Date
KDI School of Public Policy and Management
Education/Learning; Workplaces; Empirical study that tells us about how people use a system; Interview
Series Title
KDIS Working Paper 23-12
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