Decoupling in AI ethics: Learning how to walk the talk

In recent years, AI ethics declarations, commitments, and frameworks for AI systems development have proliferated. Yet, implementation remains persistently low. This phenomenon, often termed “AI ethics washing,” has been widely criticized but lacks empirical investigation. Our paper examines these gaps between declarations and operations in AI ethics through the organizational psychology concept of “decoupling”—the disconnect between what organizations say and what they do. Using data collected through in-depth interviews with 32 practitioners across diverse companies, from early-stage startups to large corporations, we present a systematic analysis of decoupling between declarations and operations in AI ethics, producing the first analysis of decoupling not only in AI ethics but in any technology development field. Our findings identify and characterize (i) common types of AI ethics declarations, such as policies and internal communications, (ii) common types of AI ethics operations, such as reviews and testing, (iii) common rationales behind companies’ approaches to AI ethics, and (iv) distinct decoupling profiles, i.e., common ways in which AI ethics declarations come apart from operations. Our discussion includes recommendations for increasing AI ethics adoption tailored to each profile. These recommendations differ from traditional AI ethics frameworks. While traditional frameworks prescribe ideal practices based on regulatory or industry expectations, this paper offers recommendations grounded in an empirical analysis of how AI ethics efforts succeed or fail in practice. Our decoupling-informed perspective fundamentally reshapes how practitioners and scholars can approach the challenge of AI ethics implementation.