Improving Fairness & Equity in COVID-19 Policy Applications of Machine Learning

As governments and social service providers attempt to understand the COVID-19 pandemic -- including the significant and asymmetrical health, social, and economic risks to their constituents -- and plan for the future through acquiring and allocating scarce resources, AI researchers and practitioners have been developing detection, forecasting, and mitigation tools to support those efforts. When policy planning and resource allocation decisions are made using these AI methods, there is a risk that they could result in inequitable and unfair outcomes for vulnerable populations. Disparate impacts of the COVID-19 pandemic on racial minorities and economically disadvantaged populations are already evident, and the risk that these disparities through applications of AI could worsen is substantial. This proposal is focused on developing bias detection/audit, reduction, and mitigation methods and tools to ensure that the policy actions taken using AI and ML reduce the risk of inequitable outcomes for vulnerable populations. While our work will be broadly applicable, we focus on four use-cases: 1) COVID-19 forecasting to improve policy decision-making, 2) identifying individuals in California facing social and economic challenges due to the epidemic, 3) understanding potential disparities in the use of contact tracing and immunity passport technologies, and 4) mental health interventions to break the cycle of incarceration in Kansas.