FAIR2-PITUN Teaching Materials (Open Access)

Integrated Administrative Data Systems (IDS) are among our most powerful tools to guide decision making for social good. By linking records across health, housing, education, and social services we can start to understand the complex challenges that poverty and marginalization carry.But data is not a neutral mirror of reality; it is a product of human systems. Without a framework that accounts for the insights of those represented in the data, even the most sophisticated analytics can inadvertently perpetuate bias and mask systemic inequality.This curriculum, supported by the Public Interest Technology University Network (PIT-UN), is designed to bridge the gap between administrative data and lived experience and between those working on data analytics and those working on the ground to drive positive social change.The five modules  apply the FAIR2 framework, moving beyond technical data standards to Frame data with community knowledge, Articulate this knowledge as assumptions in a causal map, Identify hidden biases, and Report back to the communities represented in the data.Module 1: FAIR2 Framework https://youtu.be/bXalcuNV-dEModule 2: Demographic Data and Data Chats https://youtu.be/ddugtHeTh68Module 3: Label Bias in IDS Analytics https://youtu.be/K60RDF-VhuEModule 4: Collider Bias https://youtu.be/K60RDF-VhuEModule 5: Recommendations for Social Change https://youtu.be/MazHZUf4MIQ