Artificial Intelligence Tools for Policy

90-866, Artificial Intelligence Tools for Policy

Course Description

Artificial Intelligence (AI) is the science of automating complex behaviors such as learning, problem solving, perception, and decision making. AI draws on a wide variety of disciplines, including computer science, statistics, economics, psychology, and linguistics; its contributions have been just as broad, ranging from autonomous robots to expert systems to intelligent software and web-based agents. This course will focus on applying AI methods to develop tools for intelligent problem solving in a variety of real-world application domains. Students will learn how to:

Translate policy questions into common AI problem paradigms.
Choose and apply the appropriate AI tools to solve these problems.
Correctly interpret, evaluate, and apply the results for policy analysis and decision making.

We will emphasize tools that can .scale up. to real-world policy problems involving reasoning in complex and uncertain environments, and drawing inferences from large amounts of structured, high-dimensional, and multivariate data. While many of these tools are statistical in nature, AI techniques move beyond classical statistics to build intelligent systems that can learn from experience, interact with the environment and other (human or AI) agents, and discover new and useful patterns in data.

Lecture slides

Module I: Prediction (pdf)
Module II: Modeling (pdf)
Module III: Detection (pdf)

Grading

Class participation: 5%
Project plan: 10%
Project preliminary report: 10%
Final project report: 25%
Final project presentation: 10%
Final exam: 40%

The projects will be done in teams of 2-3 people and will require the application of AI methods to real-world policy data. We plan to give students the flexibility to define their own projects, enabling them to focus on policy questions which are most relevant to their own specific interests. However, each project should consist of the following components:

Define a relevant policy question to be answered using a dataset of your choice. We will provide several example datasets, as well as other suggested sources of publicly available data.
Frame the problem in terms of one of the AI paradigms discussed in this class. Discuss this problem framework in detail, justify your choice of a problem framework, and report on methods that have been used to solve the problem in past work.
Choose an appropriate solution method for the problem. Describe the solution method in detail, compare to relate methods, and defend your choice of method.
Find, or develop, an appropriate software implementation of this method. We encourage you to use pre-existing toolkits such as Weka, though it would also be acceptable to write your own functions in Matlab, R, etc. if desired.
Evaluate your method, discussing both quantitative performance results (e.g. cross-validation error) and qualitative consideration of the usefulness of the resulting models, explanations, etc. for the given domain.
Consider extensions and variations of the original method, or alternative methods, and examine/compare their effects on performance.

Project teams will be self-selected. Typically, all team members will receive the same grade, but we may make exceptions for unevenly distributed workloads. Final project reports should contain a detailed description of the contributions of each team member to the project.

Occasionally, we will hand out short practice exercises to reinforce understanding of the course material. You do not need to turn these in. We will post answers with explanations on Blackboard, and these should help you study for the final exam.

Sample syllabus

Lecture 1: Introduction to Artificial Intelligence
Course overview
Overview of AI: viewpoints, successes, and failures
Relevance of AI for policy
Common AI paradigms
Software tools for AI

Lecture 2: Prediction and rule-based learning
The prediction problem (classification and regression)
Decision trees for classification and regression

Lecture 3: Instance-based learning
K-nearest neighbors for classification
Kernel regression
Cross-validation

Lecture 4: Model-based learning
Bayesian classification
The naive Bayes assumption

Lecture 5: Guest mini-lectures on prediction in health policy
Sean Green: Predicting the incidence of diarrheal illness
Chris Harle: Visualization and assessment of diabetes risk
Sharique Hasan: Automatic medication reconciliation
Panel discussion: using prediction for policy

Lecture 6: Representation and search
Goal-directed search: priority search and A*
State-space search: hill-climbing and simulated annealing

Lecture 7: Clustering for modeling groups
Hierarchical clustering
K-means clustering
Leader clustering

Lecture 8: Bayesian networks for modeling probabilities
Building Bayes Nets
Interpreting Bayes Nets

Lecture 9: More Bayesian networks
Inference with Bayes Nets
Learning Bayes Net structure

Lecture 10: Anomaly detection
Distance-based anomaly detection
Model-based anomaly detection
Detecting anomalies using Bayesian networks

Lecture 11: Guest mini-lectures on modeling and detection for crime policy
Kaustav Das: Detecting illicit container shipments using Bayesian networks
Wil Gorr: Detecting, predicting, and preventing crime patterns
Tom Loughran: Group-based modeling of juvenile delinquency
Panel discussion: modeling and detection for policy

Lecture 12: Biosurveillance
An exciting application of pattern detection, and your instructor's main research area.

Lecture 13: Pattern detection
Detecting patterns of anomalies
Spatial cluster detection

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