  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.
