The Principles of Data-Centric AI (DCAI)

The role of data and its quality in supporting AI systems is gaining prominence giving rise to the concept of data-centric AI (DCAI) which breaks away from widespread model-centric approaches. The flurry of conversation around DCAI can be credited to a recent campaign by Andrew Ng, an AI pioneer. DCAI is a culmination of concerns and efforts around improving data quality in AI projects. DCAI can be understood as an emerging term for a wealth of preceding practices and research work around data quality that complements structured frameworks such as human-centered data science [4,5]. As such the nature of'data work' itself is not necessarily new [35]. However, over the years, the actual data work in AI projects comes mostly from individual initiatives, and/or from piecemeal and ad hoc efforts. A lack of attention to data excellence and quality of data has resulted in underwhelming outcomes for AI systems, particularly those deployed in high-stake domains such as medical diagnosis [35]. DCAI magnifies the role of data throughout the AI lifecycle and stretches its lifespan beyond the socalled "preprocessing step" in model-centric AI.