Dissecting racial bias in an algorithm used to manage the health of populations
Dissecting racial bias in an algorithm used to manage the health of populations
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DOI：
10.1126/science.aax2342
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发表时间：
2019-10-25
期刊：
影响因子：
56.9
通讯作者：
Mullainathan, Sendhil
中科院分区：
文献类型：
--
作者：
Obermeyer, Ziad;Powers, Brian;Mullainathan, Sendhil
卫生系统依靠商业预测算法来识别和帮助有复杂健康需求的患者。我们发现，一个广泛使用的算法，典型的这种全行业的方法和影响数百万患者，表现出显着的种族偏见：在给定的风险评分，黑人患者比白色患者病情严重得多，证明了不受控制的疾病的迹象。弥补这一差距将使接受额外帮助的黑人患者比例从17.7%增加到46.5%。这种偏见的产生是因为算法预测的是医疗费用而不是疾病，但获得医疗服务的不平等意味着我们花在黑人患者身上的钱比花在白色患者身上的钱少。因此，尽管医疗保健费用似乎是一个有效的代理健康的一些措施的预测准确性，大的种族偏见出现。我们认为，在许多情况下，选择方便的，看似有效的代理地面真相可能是算法偏见的一个重要来源。
Health systems rely on commercial prediction algorithms to identify and help patients with complex health needs. We show that a widely used algorithm, typical of this industry-wide approach and affecting millions of patients, exhibits significant racial bias: At a given risk score, Black patients are considerably sicker than White patients, as evidenced by signs of uncontrolled illnesses. Remedying this disparity would increase the percentage of Black patients receiving additional help from 17.7 to 46.5%. The bias arises because the algorithm predicts health care costs rather than illness, but unequal access to care means that we spend less money caring for Black patients than for White patients. Thus, despite health care cost appearing to be an effective proxy for health by some measures of predictive accuracy, large racial biases arise. We suggest that the choice of convenient, seemingly effective proxies for ground truth can be an important source of algorithmic bias in many contexts.