Neutrality Is a Myth: AI, Power, and the Future of Equity in Schools

Artificial intelligence is often described as objective, data-driven, and neutral. But AI systems are trained on historical data — and history is not neutral. In education, where discipline patterns, academic tracking, standardized testing, and intervention systems already reflect long-standing inequities, AI has the potential to scale those disparities under the guise of efficiency and objectivity. This session examines how claims of neutrality can obscure embedded power dynamics in AI design and deployment. Participants will explore how algorithms encode assumptions about risk, merit, behavior, and readiness — and what it means to build equity-centered safeguards into emerging technologies. Rather than asking whether AI is biased, this session challenges leaders to interrogate whose values are being embedded into the systems shaping the future of schools.