Abstract

Accumulating evidence demonstrates the impact of bias that reflects social inequality on the performance of machine learning (ML) models in health care. Given their intended placement within healthcare decision making more broadly, ML tools require attention to adequately quantify the impact of bias and reduce its potential to exacerbate inequalities. We suggest that taking a patient safety and quality improvement approach to bias can support the quantification of bias-related effects on ML. Drawing from the ethical principles underpinning these approaches, we argue that patient safety and quality improvement lenses support the quantification of relevant performance metrics, in order to minimize harm while promoting accountability, justice, and transparency. We identify specific methods for operationalizing these principles with the goal of attending to bias to support better decision making in light of controllable and uncontrollable factors.

中文翻译：

---

患者安全和质量改进：针对医疗保健机器学习偏差的监管方法的道德原则。

 抽象的

越来越多的证据表明，反映社会不平等的偏见对医疗保健领域机器学习 (ML) 模型的性能产生影响。考虑到机器学习工具在更广泛的医疗保健决策中的预期地位，需要注意充分量化偏见的影响并减少其加剧不平等的可能性。我们建议对偏倚采取患者安全和质量改进方法可以支持量化偏倚相关对 ML 的影响。从支撑这些方法的道德原则出发，我们认为患者安全和质量改进镜头支持相关绩效指标的量化，以便在促进问责制、正义和透明度的同时最大限度地减少伤害。我们确定了实施这些原则的具体方法，目的是消除偏见，以支持根据可控和不可控因素做出更好的决策。