Clinical algorithms, racism, and “fairness” in healthcare: A case of bounded justice

Abstract

To date, attempts to address racially discriminatory clinical algorithms have largely focused on fairness and the development of models that “do no harm.” While the push for fairness is rooted in a desire to avoid or ameliorate health disparities, it generally neglects the role of racism in shaping health outcomes and does little to repair harm to patients. These limitations necessitate reconceptualizing how clinical algorithms should be designed and employed in pursuit of racial justice and health equity. A useful lens for this work is bounded justice, a concept and research analytic proposed by Melissa Creary to guide multidisciplinary health equity interventions. We describe how bounded justice offers a lens for (1) articulating the deep injustices embedded in the datasets, methodologies, and sociotechnical infrastructure underlying design and implementation of clinical algorithms and (2) envisioning how these algorithms can be redesigned to contribute to larger efforts that not only address current inequities, but to redress the historical mistreatment of communities of color by biomedical institutions. Thus, the aim of this article is two-fold. First, we apply the bounded justice analytic to fairness and clinical algorithms by describing structural constraints on health equity efforts such as medical device regulatory frameworks, race-based medicine, and racism in data. We then reimagine how clinical algorithms could function as a reparative technology to support justice and empower patients in the healthcare system.

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