  While there have been a number of remarkable breakthroughs in machine
learning (ML), much of the focus has been placed on model development. However,
to truly realize the potential of machine learning in real-world settings,
additional aspects must be considered across the ML pipeline. Data-centric AI
is emerging as a unifying paradigm that could enable such reliable end-to-end
pipelines. However, this remains a nascent area with no standardized framework
to guide practitioners to the necessary data-centric considerations or to
communicate the design of data-centric driven ML systems. To address this gap,
we propose DC-Check, an actionable checklist-style framework to elicit
data-centric considerations at different stages of the ML pipeline: Data,
Training, Testing, and Deployment. This data-centric lens on development aims
to promote thoughtfulness and transparency prior to system development.
Additionally, we highlight specific data-centric AI challenges and research
opportunities. DC-Check is aimed at both practitioners and researchers to guide
day-to-day development. As such, to easily engage with and use DC-Check and
associated resources, we provide a DC-Check companion website
(https://www.vanderschaar-lab.com/dc-check/). The website will also serve as an
updated resource as methods and tooling evolve over time.
