学术文献库

Within the current AI ethics discourse, there is a gap in empirical research on understanding how AI practitioners understand ethics and socially organize to operationalize ethical concerns, particularly in the context of AI start-ups. This gap intensifies the risk of a disconnect between scholarly research, innovation, and application. This risk materializes acutely as mounting pressures to identify and mitigate the potential harms of AI systems have created an urgent need to assess and implement socio-technical innovation for fairness, accountability, and transparency. Building on social practice theory, we address this need via a framework that allows AI researchers, practitioners, and regulators to systematically analyze existing cultural understandings, histories, and social practices of ethical AI to define appropriate strategies for effectively implementing socio-technical innovations. Our contributions are threefold: 1) we introduce a practice-based approach for understanding ethical AI; 2) we present empirical findings from our study on the operationalization of ethics in German AI start-ups to underline that AI ethics and social practices must be understood in their specific cultural and historical contexts; and 3) based on our empirical findings, we suggest that ethical AI practices can be broken down into principles, needs, narratives, materializations, and cultural genealogies to form a useful backdrop for considering socio-technical innovations.