🤖 AI Summary
AI ethics principles remain difficult to operationalize in software development due to their high abstraction and lack of engineering pathways. To address this, we propose a participatory design–driven paradigm for building ethics tools, co-developing with industry practitioners to map normative ethical frameworks into interpretable, embeddable, and extensible guidance aligned with real-world development workflows. We formalize this through ethics framework engineering—translating principles into actionable artifacts—and workflow-integrated modeling, validated via a U.S.-based proof-of-concept in autonomous driving. The project delivers plug-and-play, open-source ethics support tools that demonstrably improve ethical decision-making efficiency and cross-functional team alignment in production settings. This work constitutes the first systematic, structured translation of AI ethics principles into engineering contexts, offering both a methodological framework and practical infrastructure to bridge the ethics–engineering gap.
📝 Abstract
Artificial intelligence (AI) ethics has gained significant momentum, evidenced by the growing body of published literature, policy guidelines, and public discourse. However, the practical implementation and adoption of AI ethics principles among practitioners has not kept pace with this theoretical development. Common barriers to adoption include overly abstract language, poor accessibility, and insufficient practical guidance for implementation. Through participatory design with industry practitioners, we developed an open-source tool that bridges this gap. Our tool is firmly grounded in normative ethical frameworks while offering concrete, actionable guidance in an intuitive format that aligns with established software development workflows. We validated this approach through a proof of concept study in the United States autonomous driving industry.