Unconscious bias affects translational research, scientific and operational principles underlying each step of the translational process, and ultimately individual and public health. With respect to racial data, algorithmic bias built into predictive models and re-presented via equity dashboards, may yield unintended second-order consequences that subjugate planned first-order intentions. Join your colleagues to discuss leveraging racial data, in research, with equity-minded intentionality. Seminar Learning Objectives At the conclusion of this seminar, you should be able to:• Engage in impactful exchanges about unconscious bias, in research contexts;• Develop strategies for leveraging racial data in research with equity-mindedness;• Recognize, interrogate, and mitigate algorithmic biases that inform and guide research and decisions, thereby perpetuating inequities in health care access and outcomes; and• Critically examine structural racism that contributes to and is reflected by data, modeling strategies, and interpretive schemas in clinical research and translational sciences. Speaker(s): Monique L. Snowden, PhDProvost & Senior Vice President, Fielding Graduate UniversityTeaching Faculty, USC Equity Institutes