The COVID-19 pandemic has highlighted the disproportionate burden that some diseases and conditions – such as diabetes, metabolic syndrome and mental health disorders – have on historically excluded and marginalized communities. He also drew attention to the negative effects of implicit biases and the social construction of race.
The American Society for Biochemistry and Molecular Biology symposiums will examine the effects of implicit biases on science at the genetic level, including experimental design and data interpretations, and how they contribute to health disparities. This topic is of particular interest with the emerging use of genetics in the development of artificial intelligence mechanisms.
We must seek remedies and reduce health inequalities. This means asking tough questions, even from ourselves as scientists. We must examine how our implicit biases distort our lenses as biomedical researchers. We must revisit our scientific past to better understand our present, and thus prepare for our future.
Key words: Genetics, race, implicit bias, data interpretation, health disparities, artificial intelligence.
theme song: “Free Your Mind” by En Vogue is a song that talks about the everyday stereotypes, implicit biases, and micro-aggressions that have historically excluded the marginalized and those they face. If only those who make such judgments free their minds, then peace will follow for us all.
This session is underpinned by our need, as scientists, to be aware of our own implicit biases – and the potential roles they play in our research questions, experimental designs, and data analysis – so that we can mitigate them and thus health disparities.
TBA . Speakers
Race as a human structure: We are only human, not a race
Kayanta Johnson Winters (chair), The University of Texas at Arlington
Amanda Bryant FriedrichAnd the Wayne State University
Chris JegnoAnd the University of Colorado Anschutz Medical Campus
Daniel DawesAnd the Morehouse School of Medicine Satcher Institute for Health Leadership
Alison C. Augustus WallaceAnd the Louisiana State University New Orleans Health Sciences Center
How selection bias and data interpretation contribute to disparities in health outcomes and the development of artificial intelligence
Sonia Flores (chair), University of Colorado Denver
Irene Dankwa MulanAnd the IBM Watson Health
Lucio MeliAnd the Louisiana State University New Orleans Health Sciences Center
Robert MobinAnd the Louisiana State University New Orleans Health Sciences Center
Rosalina BrayAnd the National Institutes of Health Office of Research Beyond the Walls