In popular usage, Data Gravity refers to the ability of a body of data to attract applications, services and other data. In this work we introduce a broader concept,"Digital Gravity"which includes not just data, but other elements of the AI/ML workflow. This concept is born out of our recent experiences in developing and deploying an AI-based decision support platform intended for use in a public health context. In addition to data, examples of additional considerations are compute (infrastructure and software), DevSecOps (personnel and practices), algorithms/programs, control planes, middleware (considered separately from programs), and even companies/service providers. We discuss the impact of Digital Gravity on the pathway to adoption and suggest preliminary approaches to conceptualize and mitigate the friction caused by it.
Overcoming Digital Gravity when using AI in Public Health Decisions