Beyond ‘AI for Social Good’ (AI4SG): social transformations—not tech-fixes—for health equity

Abstract

This paper reflects on proliferating AI for Social Good (AI4SG) initiatives, with an eye to public health and health equity. It notes that many AI4SG initiatives are shaped by the same corporate entities that incubate AI technologies, beyond democratic control, and stand to profit monetarily from their deployment. Such initiatives often pre-frame systemic social and environmental problems in tech-centric ways, while suggesting that addressing such problems hinges on more or better data. They thereby perpetuate incomplete, distorted models of social change that claim to be 'datadriven'. In the process, AI4SG initiatives may obscure or 'ethics wash' all the other uses of big data analytics and AI that more routinely serve private interests and exacerbate social inequalities. As a case in point, it discusses the prominence of health-related applications in AI and big data fields, alongside the politics of more 'upstream' versus 'downstream' health interventions.

The medical and societal impact of big data analytics and artificial intelligence applications in combating pandemics: A review focused on Covid-19

“…At the same time, as societies have learned to function remotely during the pandemic, advanced mHealth apps will become very relevant in the near future for accomplishing many health tasks such as diagnosis and health monitoring faster and from everywhere ( Galetsi et al, 2021 ), making healthcare accessible to wider populations. The development of responsible technology must be the target of all health applications, which will help society solve the major problems that affect the well-being of human populations and come closer to the broader mythology of tech-fixes for social problems ( Holzmeyer, 2021 ).…”

Artificial intelligence and health inequities in primary care: a systematic scoping review and framework

“…This serves both to build trust and enable an understanding of when and how a specific AI intervention is suitable and what can be done to optimise the equity effects of its implementation. Holzmeyer 43 Acceptance from care providers, loss of opportunity and equity Failed implementation may affect inequities both through loss of potentially equity-improving AI systems, and through pushing new technology to uncontrolled consumer products such as smartphone apps, leaving the traditional health system unable to manage increased health anxiety and care-seeking. 2 44 45 Williams et al 45 created a framework specifically focused on ensuring sustainable AI-implementation, emphasising the need to consider the system-wide external effects from new interventions.…”

Impact of Big Data Analysis on Health

“…In addi-tion, Big Data increases clinicians' capability to combine data from multiple sources, including socioeconomic data, medical equipment, and public health statistics [1] (p.51). Consequently, medical practitioners are empowered to conduct targeted health investigations that lead to higher patient outcomes [52]. Besides, the increased access to data and technologies allows medical practitioners to improve their understanding of various medical phenomena such as emerging disease patterns and develop appropriate knowledge and skills to handle them.…”