Target specification bias, counterfactual prediction, and algorithmic fairness in healthcare
This addresses bias in healthcare ML tools, which is incremental by building on metrology concepts to correct a subtle but harmful issue.
The paper identifies target specification bias as a pervasive source of bias in ML healthcare tools, where the operationalization of target variables mismatches decision-makers' definitions, leading to overestimated accuracy and harmful patient decisions.
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.