The COVID-19 pandemic has highlighted the disproportionate burden that certain diseases and conditions such as diabetes, metabolic syndrome, and mental health disorders place on historically excluded and marginalized communities. It also looks at the negative effects of implicit prejudices and racial social constructs.
A symposium of the American Committee on Maximized Access to Biochemistry and Molecular Biology, to be held in March at Discover BMB in Seattle, discusses the impact of implicit biases on science at the genomic level, including experimental design and data interpretation, and their implications. examines how health contributes to health inequalities. This topic is of particular importance in emerging uses of genetics in developing artificial intelligence mechanisms.
We must seek remedies and reduce health disparities. This means asking difficult questions, even to ourselves as scientists. We must examine how our implicit biases distort our lens as biomedical researchers. To better understand the present and prepare for the future, we need to revisit the science of the past.
keyword: genetics, race, implicit bias, data interpretation, health disparities, artificial intelligence.
Theme song: En Vogue’s ‘Free your Mind’ is a song about everyday stereotypes, unspoken biases and microaggressions faced by historically excluded and marginalized people. If only those who make such judgments can free their minds, peace will come to all of us.
This session is based on our need as scientists to be mindful of our implicit biases and the potential role they play in research questions, experimental design and data analysis.
Race as a Human Construct: We’re Just Humans, Not a Race
Kayunta Johnson – Winters (Chair), University of Texas at Arlington
Amanda Bryant-Friedrich, Wayne State University
Chris Ginoux, University of Colorado Anschutz Medical Campus
Daniel Dawes, Morehouse College of Medicine Thatcher Health Leadership Institute
Alison C. Augustus-Wallace, Louisiana State University Health Science Center New Orleans
How selection biases and data interpretation contribute to disparities in health outcomes and artificial intelligence development
Sonia Flores (Chair), University of Colorado Denver
Eileen Danqua-Mullan, IBM Watson Health
Lucio Miele, Louisiana State University Health Science Center New Orleans
Robert Maupin, Louisiana State University Health Science Center New Orleans
Rosalina Bray, Office of Extramural Research, National Institutes of Health