The COVID-19 pandemic has shone a light on the disproportionate burden that certain diseases and conditions — such as diabetes, metabolic syndrome and mental health disorders — have on historically excluded, marginalized communities. It also has drawn attention to the negative impacts of implicit biases and the social construct of race.
The American Society for Biochemistry and Molecular Biology Maximizing Access Committee’s symposia at Discover BMB in Seattle in March will examine the effects of implicit biases on science at the genomic level, including experimental design and data interpretations, and how they contribute to health disparities. This topic is of particular importance with the emerging use of genetics in the development of artificial intelligence mechanisms.
We must seek remedies and mitigate health disparities. This means asking tough questions, even of ourselves as scientists. We must examine how our implicit biases warp our lens as biomedical researchers. We must revisit our scientific past to better understand our present and, thus, prepare for our future.
Keywords: Genetics, race, implicit bias, data interpretation, health disparities, artificial intelligence.
Theme song: “Free your Mind” by En Vogue is a song that speaks to daily stereotypes, implicit biases and microaggressions that historically excluded, marginalized people face. If only those who make such judgments would free their minds, peace for all of us would follow.
This session is powered by our need, as scientists, to be mindful of our implicit biases — and the potential roles they play in our research questions, experimental designs and data analyzes — so that we can mitigate them and thereby health disparities.
Race as a human construct: We are only human, not a race
Kayanta Johnson–Winters (chair), University of Texas at Arlington
Amanda Bryant–Friedrich, Wayne State University
Chris Gignoux, University of Colorado Anschutz Medical Campus
Daniel Dawes, Morehouse School of Medicine Satcher Health Leadership Institute
Allison C. Augustus–Wallace, Louisiana State University Health Sciences Center New Orleans
How selection bias and data interpretation contribute to disparities in health outcomes and artificial intelligence development
Sonia Flores (chair), University of Colorado Denver
Irene Dankwa–Mullan, IBM Watson Health
Lucio Miele, Louisiana State University Health Sciences Center New Orleans
Robert Maupin, Louisiana State University Health Sciences Center New Orleans
Rosalina Bray, National Institutes of Health Office of Extramural Research