Overcoming Digital Gravity when using AI in Public Health Decisions

TL;DR

This paper introduces the concept of Digital Gravity, encompassing data and AI/ML workflow elements, and discusses its impact on deploying AI in public health, proposing ways to address associated challenges.

Contribution

It broadens the understanding of Digital Gravity beyond data, highlighting its influence on AI deployment in public health and suggesting mitigation strategies.

Findings

Digital Gravity affects AI deployment pathways.

Mitigation approaches can reduce deployment friction.

Broader AI ecosystem elements are integral to Digital Gravity.

Abstract

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.