Happy Black History Month! The Seminar does not have any scheduled guests or podcasts so far this month, and so an opportunity arises to highlight voices & publications beyond our venerable (& growing!) list of participants. During this strange & stressful February, I wanted to make space, as SE (Shack) Hackney did last year, within Information Ecosystems to highlight some incredible and essential work by and about Black voices, and—amid a global pandemic—how race overlaps with medicine, data, and concepts of cure. What follows is an absolutely non-exhaustive reading list on topics of Blackness, medicine, data, and technology. I offer these pieces & voices as profoundly important to how we should be thinking about medicine and technology within our current moment; it is difficult to understate the debt we all owe to Black scholars, activists, scientists, doctors, and organizers, particularly in digitally-oriented spaces—but lending an eye or ear to their essential contributions is a start.
And indeed, as the long shadow of COVID-19 extends toward its year-long mark, we must take seriously the disproportionally devastating impact the pandemic has had on our nation’s Black communities. Today, while the rate of hospitalization and death per 10,000 sits at 7.4 and 2.3 for white patients, it is a staggering 24.6 and 5.6 for Black patients (source). Scholars from a wide array of disciplines have over and over confirmed that the U.S. has a long and difficult history of racism in medicine. And, as our own Seminar guests—such as Dr. Safiya Noble and Dr. Sandra González-Bailón—have also confirmed, the algorithms and AI which intersect increasingly often with medical diagnoses and treatment are, too, anything but anti-racist. Because these algorithms are trained by data that significantly over-represent cis-gendered, urban-dwelling white bodies, it is quite possible for such algorithms to, for example, misdiagnose a Black patient as free from skin cancer; and because medical degree-granting programs are so profoundly lacking in adequate racial and cultural training, a Doctor might not catch what an algorithm does not, either. The result of all this is a medical landscape that is outright hostile to Black patients—and we owe it to ourselves and our communities to read up on our shared histories.
The books & articles below span a wide variety of genres, topics, and approaches to race, medicine, and technology; some, like Audre Lorde’s The Cancer Journals, share personal testimonies of being Black, sick, and a woman in the 20th-century U.S.–essential context for understanding not only where we are today, but how we got here. Others, Ruha Benjaim’s Race After Technology, importantly inspect the contemporary medical landscape’s overlap with Big Data & Artificial Intelligence. And studies like Obermeyer et al.’s groundbreaking “Dissecting Racial Bias…” lay bare the very real material consequences of relying on inherently biased algorithmic tools. I hope you get as much from reading them as I have.
Books
Alondra Nelson, Body and Soul: The Black Panther Party and the Fight against Medical Discrimination
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Harriet A. Washington, Medical Apartheid: The Dark History of Medical Experimentation on Black Americans from Colonial Times to the Present
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Dorothy Roberts, Killing the Black Body: Race, Reproduction, and the Meaning of Liberty ; Fatal Invention: How Science, Politics, and Big Business Re-create Race in the Twenty-First Century ; Shattered Bonds: The Color of Child Welfare
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Tina K. Sacks, Invisible Visits: Black Middle-Class Women in the American Healthcare System
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Ruha Benjamin, Race After Technology: Abolitionist Tools for the New Jim Code
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Ruha Benjamin (editor), Captivating Technology: Race, Carceral Technoscience, and Liberatory Imagination in Everyday Life
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Audre Lorde, The Cancer Journals
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C. Riley Snorton, Black on Both Sides: A Racial History of Trans Identity
Articles
Obermeyer, Ziad, et al. “Dissecting Racial Bias in an Algorithm Used to Manage the Health of Populations”
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Joia Crear-Perry et al., “Moving Toward Anti-Racist Praxis in Medicine”
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Rajkomar, Alvin et al. “Ensuring Fairness in Machine Learning to Advance Health Equity.”
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Paulus, J.K. and Kent, D.M. “Predictably Unequal: Understanding and Addressing Concerns that Algorithmic Clinical Prediction May Increase Health Disparities”
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Eliane Röösli et al., “Bias at Warp Speed: How AI May Contribute to the Disparities Gap in the Time of COVID-19”