Effecting Change Through Policymaking
Instructor information
Dr. José Marichal (he/him/his)
Professor of Political Science
Contact: marichal@callutheran.edu
Course Description: The Policy Process
The policy-making process is an organized series of steps designed to bring about specific results, transforming political goals into actionable public policies. This course explores the art of policy-making through the lens of artificial intelligence. We will examine how AI can be deployed across the four critical phases of policy development: Agenda-setting, Formulation, Implementation, and Evaluation.
Students will learn to navigate the complexities of policy-making, stakeholder resistance, and institutional factors, using AI not as a content generator, but as a tactical engine for **Institutional Capture & Bureaucratic Hacking**. We move from simply drafting proposals to identifying "legal hacks" and orphaned mandates designed to bypass legislative bottlenecks and force administrative compliance.
Course Objectives
By the end of this course, students will be able to:
- Agenda-Setting & Discovery: Deploy AI systems for persistent jurisdictional monitoring and issue identification.
- Policy Formulation: Use LLMs to translate technical policy needs into politically viable drafts and proposed regulations.
- Policy Implementation: Coordinate efforts and manage stakeholder resistance during the execution phase using AI mobilization simulations.
- Policy Evaluation: Critically assess policy outcomes and the boundaries of automated influence in democratic participation.
Course Assignments
Total Points: 100
- Issue Monitoring Dashboard (20 Points) [Agenda-setting]: Build an automated system to track a specific policy issue across jurisdictions using automated monitoring of minutes and filings.
- Legislative Drafting Portfolio (25 Points) [Formulation]: Retrieve a model policy and use AI to draft variants, searching for the "Goldilocks zone" of support through comparative analysis.
- Regulatory Comment Bot (25 Points) [Implementation]: Design a system to generate 1,000+ unique, substantive regulatory comments, aiding in public education campaigns and influencing administrative decisions.
- Final Project: Policy Evaluation Simulation (30 Points) [Evaluation]: Move the "Code" of your proposal through a simulated (or real) legislative gauntlet, assessing its outcomes and effectiveness.
Schedule
Identifying emerging issues and setting the groundwork for policy development.
Unit 1: The Intelligence Layer
The structural shift in advocacy and identifying issues that require intervention.
Automated monitoring of city council minutes and regulatory filings. Making problems visible.
Unit 2: Narrative Intelligence
Synthesizing intelligence. Using AI sentiment analysis to test which policy frames resonate.
Presentation of issues and setting the agenda through automated dashboards.
Developing viable policy solutions through "Bureaucratic Hacking" and securing strategic agreements.
Unit 3: Adapting Policy Frameworks
Using AI to analyze thousands of pages of existing municipal code to find "legal hacks," orphaned mandates, and forgotten enforcement triggers that can be used to bypass legislative bottlenecks.
Draft bills and actionable regulations. Finding the optimal support zone.
Unit 4: Coalition Building
Analyzing profiles of decision-makers and anticipating political pressures.
Identifying key leverage points in the committee process to push long-term solutions.
Executing policies through administrative decisions and operations.
Unit 5: Converting Policies to Operations
Using AI to coordinate service delivery, stakeholder alignment, and practical applications.
Navigating the transition from legislation to implementation. Government departments and agencies.
Unit 6: Administration & Participation
Generating unique regulatory comments. Automating public education campaigns.
Tracking for enforcement gaps and maintaining regulatory compliance.
Assessing outcomes and determining necessary adjustments.
Unit 7: The Long Game
Case Study: **OpenAI's Democratic Inputs Program** (2023). We examine procedures for using AI to scale up the process of moderating focus groups and synthesizing participant statements while preserving minority viewpoints.
Using AI to understand policy effectiveness and overcoming opposition from interest groups.
Unit 8: The Feedback Loop
Making modifications based on new information and adapting to changing societal needs.
Moving your proposal through a simulated legislative gauntlet and conducting a full evaluation.