This Virtual Workgroup Session was convened by the Coalition for Health AI to develop a collective understanding of the definitions, important considerations, and open questions for the concepts of equity, bias, and fairness in health. With input and participation from a group of subject matter experts from healthcare and other industries, this session included a series of three lightning talk presentations and group discussions centered on preselected use cases. It also featured a set of breakout sessions that addressed the themes of Health Equity by Design; Bias and Fairness Processes and Metrics; and Impacting Marginalized Groups: Mitigation Strategies for Data, Model, and Application Bias. The aim of this and other planned meetings is to develop a practical guide for implementing AI and ML tools in healthcare, one that establishes clear and appropriate guidelines and guardrails for the fair, ethical, and useful application of machine learning in healthcare settings.