As schools increasingly turn to predictive modeling and data-driven strategy to identify major gift prospects and optimize outreach, we must ask: who benefits—and who is left out? This session will explore the ethical implications of predictive analytics in Advancement. Drawing from industry data and research into algorithmic bias, we will examine how algorithmic models can unintentionally reinforce various social and cultural inequities, silence emerging voices, and entrench existing power dynamics in philanthropy. We will explore the concept of “encoded silences” — what our data excludes, and why it matters and offer practical strategies for integrating inclusive data practices and stakeholder accountability. Participants will leave with a framework for interrogating their own predictive models, questions to ask their vendors and analysts, and actionable approaches to aligning their data practices with institutional values around equity and inclusion.