AI Governance in the System Development Life Cycle: Insights on Responsible Machine Learning Engineering
This paper explores how artificial intelligence (AI) governance can be incorporated in the system development life cycle (SDLC). To this end, we conducted an expert interview study among AI professionals. We analyzed the interviews using qualitative coding and clustering to extract key AI governance concepts, and subsequently mapped these concepts onto three key stages in the machine learning (ML) system development process: (1) design, (2) development, and (3) operation. This work can be viewed as a step towards understanding how AI governance is connected to ML systems’ management process. With our findings, we contribute to the literature on ML system development and management by bridging the gap between AI governance requirements and technical implementation.