PA 470 – Artificial Intelligence and Machine Learning in the Public Sector
Public sector applications of artificial intelligence and machine learning. Philosophical foundations of artificial intelligence and major frameworks for learning.
Artificial intelligence (AI) and machine learning (ML) have transformed the private sector pressuring the public sector to follow suit and become smart by adopting AI/ML. Yet, public sector applications differ greatly from private sector ones where issues like data availability and equity can be easily sidestepped. Key assumptions of AI/ML models often conflict with traditional principles of government like transparency, accountability, universality, and equality/equity.
In this course, we will explore critical issues with AI/ML in the public sector and learn simple applications in R and tidymodels to provide public sector technologists with adequate tools to navigate AI/ML in government.
Assignments and readings should be completed before the meeting in class for that week, unless otherwise noted.
Week 1, 1/11
Introductions, Course Technology Stack, and Review
- Can a Machine Learn Morality?
- Crime Prediction Software Promised to Be Free of Biases. New Data Shows It Perpetuates Them
- Chapter 6, Data Science for Public Policy (DSPP)
Assignment: Coding Warmup 0
Week 2, 1/18
Review, Conceptual Foundations, and Geospatial
- tidymodels, Chapters 1-2
- Chapter 12, DSPP
- sf
- tidycensus
- Urban Institute Guide
- Racism In, Racism Out
Assignment: Coding Warmup 1
Week 3, 1/25
Coding 1
- tidymodels, Chapters 3-4
- Chapter 7 & Sections 8.1/8.2, DSPP
Assignment: Coding Warmup 2
Week 4, 2/1
- Chapter 8, DSPP (continued)
- tidymodels, Chapters 5-6 Fitting/Linear
NYC & Federal AI Leadership
- New York City Artifical Intelligence Action Plan
- Summary of Agency Compliance Reporting, NYC Algorithms
- Op-Ed by Antony Blinken & Gina Raimondo: To shape the future of AI, we must act quickly
- President Biden Issues Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence
- Letter to the White House on Forthcoming AI Executive Order, Leadership Council
Assignment: Reading Response 1
Assignment: Coding Warmup 3
Week 6, 2/15
- Chapter 9, DSPP
- tidymodels, Chapter 9 & 10
Ethics: Government’s Black Box
- A City Is a City — Against the metaphorization of data
- ai.gov
- ai.gov use cases
- Crafting an AI strategy for government leaders
- Using AI and machine learning to reduce government fraud
- Fragile Algorithms and Fallible DecisionMakers: Lessons from the Justice System
- REPORT: How to make AI work in government and for people, Case Studies
- Decoding Intentions Artificial Intelligence and Costly Signals
Assignment: Response 2, Relationships
Assignment: Coding Warmup 4
Week 7, 2/22
Coding 3
Week 8, 2/29
Coding 4
- Sections 10.5-10.6, DSPP
- tidymodels, Chapter 10-11 Resampling
Assignment: Cook Part 2
Week 9, 3/7
Ethical Critiques
- Chapter 14, DSPP
Read 4
- It’s COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarks
- Excavating AI
- Assembling Accountability
- To Live in Their Utopia: Why Algorithmic Systems Create Absurd Outcomes
- Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence
- Algorithmic Risk Assessments Can Alter Human Decision-Making Processes in High-Stakes Government Contexts
Introduce Assignment: Final Project Proposal, Due 4/4
Assignment: Reading Response 3, Ideals
Week 10, 3/14
Coding 4
- Against Predictive Optimization: On the Legitimacy of Decision-Making Algorithms that Optimize Predictive Accuracy
- tidymodels, Tuning Chapter 12-15
Assignment: Cook Part 3, Due 3/25
Week 11, 3/21
Spring Vacation No Class
Week 13, 4/4
Coding 6 Natural Language
- Chapter 13, DSPP
- tidymodels, Chapter 16, 19, 20
Assignment: Final Project Proposal, Due 4/4
Week 15, 4/18
Public Sector Implementation & Large Language Models
- On the Societal Impact of Open Foundation Models
- Ch 5 Weapons of Math Destruction, on Blackboard
- Chapter 15, DSPP
Assignment: In Class Exercise 1