Better science follows when we control our thoughts

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.


implicit bias
speaker TBA

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

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