Does "AI" stand for augmenting inequality in the era of covid-19 healthcare?

TL;DR

This paper discusses how AI used in covid-19 healthcare can unintentionally reinforce existing social inequalities, especially affecting marginalized communities, due to biases in data and design.

Contribution

It identifies three ways AI can reflect or introduce bias in health disparities and emphasizes the need for bias mitigation in AI deployment during the pandemic.

Findings

AI can entrench health discrimination in datasets

AI systems often lack representativeness of vulnerable groups

Biases in AI deployment can worsen health inequities

Abstract

Among the most damaging characteristics of the covid-19 pandemic has been its disproportionate effect on disadvantaged communities. As the outbreak has spread globally, factors such as systemic racism, marginalisation, and structural inequality have created path dependencies that have led to poor health outcomes. These social determinants of infectious disease and vulnerability to disaster have converged to affect already disadvantaged communities with higher levels of economic instability, disease exposure, infection severity, and death. Artificial intelligence (AI) technologies are an important part of the health informatics toolkit used to fight contagious disease. AI is well known, however, to be susceptible to algorithmic biases that can entrench and augment existing inequality. Uncritically deploying AI in the fight against covid-19 thus risks amplifying the pandemic's adverse effects on vulnerable groups, exacerbating health inequity. In this paper, we claim that AI systems can introduce or reflect bias and discrimination in three ways: in patterns of health discrimination that become entrenched in datasets, in data representativeness, and in human choices made during the design, development, and deployment of these systems. We highlight how the use of AI technologies threaten to exacerbate the disparate effect of covid-19 on marginalised, under-represented, and vulnerable groups, particularly black, Asian, and other minoritised ethnic people, older populations, and those of lower socioeconomic status. We conclude that, to mitigate the compounding effects of AI on inequalities associated with covid-19, decision makers, technology developers, and health officials must account for the potential biases and inequities at all stages of the AI process.