  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.
