Experiential ethics training for genomic researchers

ELSIcon2024 • Panel • June 12, 2024 

Authors:

Corresponding Author: Quinn Waeiss, PhD (they/them/theirs) - Stanford University

Panelist: Ariadne Nichol, BA - Stanford Center for Biomedical Ethics

Panelist: David Magnus - Stanfor Center for Biomedical Ethics

Panelist: Katie Shilton, PhD - University of Maryland

Panelist: Michael Bernstein - Stanford University

The proliferation and continued advancement of AI tools in genomic research can both foster unique ELSI concerns and exacerbate known ELSI issues within the field of genetics and genomics. These include issues related to data representation, algorithmic bias, tool error, and the exacerbation of existing societal and health inequities. This proposed panel carries on the theme from one of the plenary sessions: addressing concerns of data bias and, more broadly, the development and deployment of algorithms in genomic research. 
As genomic researchers integrate AI tools within their work, they must be sensitive to the ELSI concerns that can arise from the ways the obtain, process, and interpret data; how the objectives embedded in their AI models are translated into decisions; and the role of adversarial applications of their research, to name a few. At the same time, many researchers can struggle with the cognitive tasks required to effectively identify, mitigate, and evaluate these and other ELSI concerns. 
Therefore, this proposed panel will provide an evidence-based platform for thinking about how to move the ethics paradigm forward and/or upstream in the scientific process, including embedded ELSI requirements in large genomic projects. It will include members of three different experiential ethics interventions from two different institutions. Each panelist will describe the motivation for, procedural elements of, and results from initial evaluations of their proposed interventions. 
The first will discuss a project aimed at encouraging value discussions and perspective-taking among development teams. 

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