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.

本文探讨了 AI 在应对 covid-19 疫情中的作用与潜在问题，指出 AI 系统可能会在三个层面引入或反映偏见和歧视，从而加剧疫情对处境困难群体的不利影响。作者呼吁决策者、技术开发人员和卫生官员应全程考虑 AI 过程中的潜在偏差和不公平性，以减轻 AI 对 covid-19 不平等的复合影响。