Racial and ethnic minorities have borne a particularly acute burden of the COVID-19 pandemic in the United States. There is a growing awareness from both researchers and public health leaders of the critical need to ensure fairness in forecast results. Without careful and deliberate bias mitigation, inequities embedded in data can be transferred to model predictions, perpetuating disparities, and exacerbating the disproportionate harms of the COVID-19 pandemic. These biases in data and forecasts can be viewed through both statistical and sociological lenses, and the challenges of both building hierarchical models with limited data availability and drawing on data that reflects structural inequities must be confronted. We present an outline of key modeling domains in which unfairness may be introduced and draw on our experience building and testing the Google-Harvard COVID-19 Public Forecasting model to illustrate these challenges and offer strategies to address them. While targeted toward pandemic forecasting, these domains of potentially biased modeling and concurrent approaches to pursuing fairness present important considerations for equitable machine-learning innovation.

种族和少数民族在美国承受了特别严重的 COVID-19 大流行负担。研究人员和公共卫生领导人越来越意识到确保预测结果公平的迫切需要。如果不仔细和刻意地缓解偏见，数据中嵌入的不公平现象可能会转移到模型预测中，使差异长期存在，并加剧 COVID-19 大流行的不成比例的危害。数据和预测中的这些偏差可以通过统计和社会学的视角来看待，并且必须面对建立具有有限数据可用性的分层模型和利用反映结构性不平等的数据的挑战。我们概述了可能引入不公平的关键建模领域，并利用我们构建和测试 Google-Harvard COVID-19 公共预测模型的经验来说明这些挑战并提供解决这些挑战的策略。虽然针对大流行预测，但这些可能存在偏见的建模领域和追求公平的并行方法为公平的机器学习创新提出了重要的考虑。