Bias in applications of machine learning (ML) to healthcare is usually attributed to unrepresentative or incomplete data, or to underlying health disparities. This article identifies a more pervasive source of bias that affects the clinical utility of ML-enabled prediction tools: target specification bias. Target specification bias arises when the operationalization of the target variable does not match its definition by decision makers. The mismatch is often subtle, and stems from the fact that decision makers are typically interested in predicting the outcomes of counterfactual, rather than actual, healthcare scenarios. Target specification bias persists independently of data limitations and health disparities. When left uncorrected, it gives rise to an overestimation of predictive accuracy, to inefficient utilization of medical resources, and to suboptimal decisions that can harm patients. Recent work in metrology - the science of measurement - suggests ways of counteracting target specification bias and avoiding its harmful consequences.

机器学习在医疗保健中的偏见通常源于代表性或不完整的数据，以及潜在的健康差异。本文发现了影响机器学习预测工具的临床实用性的更普遍的偏见来源：目标规范偏见。目标规范偏见发生在目标变量的操作化与决策者对其定义的不匹配时，这种不匹配常常是微妙的，源于决策者通常对反事实的医疗场景的预测结果感兴趣，而不是实际情况。目标规范偏见独立于数据限制和健康差异。如果不进行修正，它会导致预测准确度的高估，医疗资源的低效利用以及对患者有害的次优决策。计量学中的最新研究提出了抵消目标规范偏见的方法，并避免其有害后果。

目标规范偏差、反事实预测和医疗算法公平性