The ethics of generative AI in social science research: A qualitative approach for institutionally grounded AI research ethics

Abstract

Despite growing attention to the ethics of Generative AI, there has been little discussion about how research ethics should be updated for social science research practice. 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, and institutionally grounded research ethics principles are necessary. In all of the narratives, the technical functions of Generative AI were merely necessary conditions of unethical practices, while ethical dilemmas started to arise when such functions were situated in the institutional arrangements of academia. Our findings suggest that the ethics of AI-assisted research should encompass not only specific ethical rules concerning AI functionalities but also incorporate community engagement, educational imperatives, institutional governance, and the societal impact of such technologies to organize “ethics-in-practice.” This will require democratic deliberations to address the complex, emergent interactions between AI systems and societal structures.