The Mechanics of Algorithmic Governance: From Neural Networks to Foundation Models

The Mechanics of Algorithmic Governance: From Neural Networks to Foundation Models Explore the critical shift from traditional causal inference to predictive machine learning models, and the systemic risks of emergence and homogenization in large-scale foundation models. We analyze how the NIST Framework and the White House AI Bill of Rights guide public sector deployments. 🎓 Class Connection: This is the official video overview for PAD 747: AI Policy and Regulation (Governance, Law, and Public Administration) at CUNY John Jay College of Criminal Justice. 🔗 Access the course resources, readings, and public policy toolkits at: https://reWandt.com TIMESTAMPS: 0:00 - Introduction & Video Start 1:42 - Causal Inference vs. Machine Learning 3:19 - Foundation Models & Homogenization 4:37 - Algorithmic Risk in Pretrial Decisions 5:33 - NIST AI Risk Management Framework (AI RMF) 6:37 - White House Blueprint for an AI Bill of Rights 7:25 - Conclusion & Outro ⚖️ DISCLAIMER: This video was generated using artificial intelligence narration and compilation. While we make every effort to ensure the accuracy and correctness of the courseware and materials presented, minor errors or incongruities may occasionally occur. The content and presentation do not necessarily represent the official viewpoints or personal opinions of Professor Adam Scott Wandt. #reWandt #AIPolicy #AIGovernance #JohnJayCollege #PublicAdministration