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Assessing Risk, Automating Racism a Health Care Algorithm Reflects Underlying Racial Bias in Society

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Assessing Risk, Automating Racism a Health Care Algorithm Reflects Underlying Racial Bias in Society Racial bias in cost data leads an algorithm to underestimate health care needs of Black patients. Downloaded from SOCIAL SCIENCE Assessing risk, automating racism http://science.sciencemag.org/ A health care algorithm reflects underlying racial bias in society By Ruha Benjamin era, the intention to deepen racial inequi- beyond the algorithm developers by con- ties was more explicit, today coded ineq- structing a more fine-grained measure of s more organizations and indus- uity is perpetuated precisely because those health outcomes, by extracting and clean- tries adopt digital tools to identify who design and adopt such tools are not ing data from electronic health records to risk and allocate resources, the au- thinking carefully about systemic racism. determine the severity, not just the number, tomation of racial discrimination is Obermeyer et al. gained access to the train- of conditions. Crucially, they found that so a growing concern. Social scientists ing data, algorithm, and contextual data for long as the tool remains effective at pre- on February 4, 2020 have been at the forefront of study- one of the largest commercial tools used by dicting costs, the outputs will continue to Aing the historical, political, economic, and health insurers to assess the health profiles be racially biased by design, even as they ethical dimensions of such tools (1–3). But for millions of patients. The purpose of the may not explicitly attempt to take race into most analysts do not have access to widely tool is to identify a subset of patients who re- account. For this reason, Obermeyer et al. used proprietary algorithms and so can- quire additional attention for complex health engage the literature on “problem formula- not typically identify the precise mecha- needs before the situation becomes too dire tion,” which illustrates that depending on nisms that produce disparate outcomes. and costly. Given increased pressure by the how one defines the problem to be solved— On page 447 of this issue, Obermeyer et Affordable Care Act to minimize spending, whether to lower health care costs or to al. (4) report one of the first studies to most hospital systems now utilize predictive increase access to care—the outcomes will examine the outputs and inputs of an al- tools to decide how to invest resources. In vary considerably. gorithm that predicts health risk, and in- addition to identifying the precise mecha- To grasp the broader implications of the fluences treatment, of millions of people. nism that produces biased predictions, Ober- study, consider this hypothetical: The year They found that because the tool was de- meyer et al. were able to quantify the racial is 1951 and an African American mother of signed to predict the cost of care as a proxy disparity and create alternative algorithmic five, Henrietta Lacks, goes to Johns Hopkins for health needs, Black patients with the predictors. Hospital with pain, bleeding, and a knot same risk score as White patients tend to Practically speaking, their finding means in her stomach. After Lacks is tested and be much sicker, because providers spend that if two people have the same risk score treated with radium tubes, she is “digitally much less on their care overall. This study that indicates they do not need to be enrolled triaged” (2) using a new state-of-the-art risk contributes greatly to a more socially con- in a “high-risk management program,” the assessment tool that suggests to hospital S E scious approach to technology develop- health of the Black patient is likely much staff the next course of action. Because the G MA I ment, demonstrating how a seemingly worse than that of their White counterpart. tool assesses risk using the predicted cost TY T benign choice of label (that is, health cost) According to Obermeyer et al., if the predic- of care, and because far less has commonly E G / A initiates a process with potentially life- tive tool were recalibrated to actual needs been spent on Black patients despite their R ME threatening results. Whereas in a previous on the basis of the number and severity of actual needs, the automated system un- A C T A active chronic illnesses, then twice as many derestimates the level of attention Lacks F O: T Department of African American Studies, Princeton Black patients would be identified for inter- needs. On the basis of the results, she is O H P University, Princeton, NJ, USA. Email: [email protected] vention. Notably, the researchers went well discharged, her health rapidly deteriorates, SCIENCE sciencemag.org 25 OCTOBER 2019 • VOL 366 ISSUE 6464 421 Published by AAAS INSIGHTS | PERSPECTIVES and, by the time she returns, the cancer has they are valued less (9). It is not “something BATTERY TECHNOLOGY advanced considerably, and she dies. about the interactions that Black patients This fictional scenario ends in much the have with the healthcare system” that leads same way as it did in reality, as those familiar to poor care, but the persistence of structural The coming with Lacks’s story know well (5–7). But rather and interpersonal racism. Even health care than getting assessed by a seemingly race- providers hold racist ideas, which are passed neutral algorithm applied to all patients in down to medical students despite an oath electric vehicle a colorblind manner, she was admitted into to “do no harm” (10). The trope of the “non- the Negro wing of Johns Hopkins Hospital compliant (Black) patient” is yet another way during a time when explicit forms of racial that hospital staff stigmatize those who have transformation discrimination were sanctioned by law and reason to question medical authority (11, 12). custom—a system commonly known as Jim But a “lack of trust” on the part of Black pa- A future electric transporta- Crow. However, these are not two distinct tients is not the issue; instead, it is a lack of tion market will depend on processes, but rather Jim Crow practices feed trustworthiness on the part of the medical in- the “New Jim Code”—automated systems that dustry (13). The very designation “Tuskegee battery innovation hide, speed, and deepen racial discrimination study” rather than the official name, U.S. Pub- behind a veneer of technical neutrality (1). lic Health Service Syphilis Study at Tuskegee, By George Crabtree1,2 Data used to train automated systems are continues to hide the agents of harm. Ober- typically historic and, in the context of health meyer et al. mention some of this context, but lectric vehicles are poised to trans- care, this history entails segregated hospital passive and sanitized descriptions continue form nearly every aspect of transporta- Downloaded from facilities, racist medical curricula, and un- to hide the very social processes that make tion, including fuel, carbon emissions, equal insurance structures, among other fac- their study consequential. Labels matter. costs, repairs, and driving habits. The tors. Yet many industries and organizations As researchers build on this analysis, it primary impetus now is decarboniza- well beyond health care are incorporating is important that the “bias” of algorithms tion to address the climate change automated tools, from education and bank- does not overshadow the discriminatory Eemergency, but it soon may shift to eco- ing to policing and housing, with the prom- context that makes automated tools so nomics because electric vehicles are antici- http://science.sciencemag.org/ ise that algorithmic decisions are less biased important in the first place. If individuals pated to be cheaper and higher-performing than their human counterpart. But human and institutions valued Black people more, than gasoline cars. The questions are not decisions comprise the data and shape the they would not “cost less,” and thus this tool if, but how far, electrification will go. What design of algorithms, now hidden by the might work similarly for all. Beyond this will its impact be on the energy system and promise of neutrality and with the power to case, it is vital to develop tools that move on geoeconomics? What are the challenges unjustly discriminate at a much larger scale from assessing individual risk to evaluat- of developing better batteries and securing than biased individuals. ing the production of risk by institutions so the materials supply chain to support new For example, although the Fair Housing that, ultimately, the public can hold them battery technology? Act of 1968 sought to protect people from accountable for harmful outcomes. j The signs of vehicle electrification are discrimination when they rent or buy a growing. By 2025, Norway aims to have REFERENCES AND NOTES home, today social media platforms allow 100% of its cars be either an electric or on February 4, 2020 1. R. Benjamin, Race After Technology: Abolitionist Tools marketers to explicitly target advertisements for the New Jim Code (Polity Press, 2019). plug-in hybrid unit, and the Netherlands by race, excluding racialized groups from 2. V. Eubanks, Automating Inequality: How High-Tech Tools plans to ban all gasoline and diesel car the housing market without penalty (8). Al- Profile, Police, and Punish the Poor (St. Martin’s Press, sales by the same year. By 2030, Germany 2018). though the federal government brought a 3. S. Noble, Algorithms of Oppression: How Search Engines plans to ban internal combustion engines, suit against Facebook for facilitating digital Reinforce Racism (NYU Press, 2018). and by 2040, France and Great Britain aim discrimination in this manner, more recently 4. Z. Obermeyer, B. Powers, C. Vogeli, S. Mullainathan, to end their gasoline and diesel car sales. Science 366, 447 (2019). the U.S. Department of Housing and Urban 5. K. Holloway, Private Bodies, Public Texts: Race, Gender, The most aggressive electric vehicle tar- Development introduced a rule that would and a Cultural Bioethics (Duke Univ.
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