The COVID-19 pandemic has shed light on the disproportionate burden that certain diseases and conditions – such as diabetes, metabolic syndrome and mental disorders – place on historically excluded, marginalized communities. It has also drawn attention to the negative effects of implicit prejudice and the social construct of race.
The American Society for Biochemistry and Molecular Biology’s Maximizing Access Committee symposiums at Discover BMB in Seattle in March will examine the impact of implicit biases on science at the genomic level, including experimental design and data interpretation, and how they contribute to health differences. This topic is of particular importance given the emerging use of genetics in the development of artificial intelligence mechanisms.
We need to put things right and reduce health inequalities. That means asking difficult questions, including of ourselves as scientists. We need to examine how our implicit biases distort our lens as biomedical researchers. We need to rethink our scientific past to better understand our present and thus prepare for our future.
keywords: genetics, race, implied bias, data interpretation, health differences, artificial intelligence.
theme song: “Free Your Mind” by En Vogue is a song that addresses everyday stereotypes, implicit prejudices and micro-aggressions that historically excluded, marginalized people face. If only those who make such judgments would clear their minds, peace would follow for all of us.
This session is driven by our need as scientists to be aware of our implicit biases—and the potential role they play in our research questions, experimental designs, and data analysis—so that we can mitigate them, and thus health disparities.
Race as a Human Construct: We are only human, not a race
Kayunta 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, Center for Health Sciences at Louisiana State University New Orleans
How selection bias and data interpretation contribute to differences in health outcomes and artificial intelligence development
Sonja Flores (Chair), University of Colorado Denver
Irene Dankwa-Mullan, IBM Watson Health
Lucio Miele, Center for Health Sciences at Louisiana State University New Orleans
Robert Maupin, Center for Health Sciences at Louisiana State University New Orleans
Rosalina Bray, National Institutes of Health Office of Extramural Research