Artificial Intelligence Methods for Social Good (Spring 2018)

Poster [jpg]


For students on the waitlist: some course materials in the first few weeks are available on this webpage. Check the references in the course schedule section below.


Please fill the auditing form. Please note that the classroom has limited seats and priority will be given to the students who registered for the class.


Some lectures will be recorded and can be accessed through Panopto with an Andrew ID [link].

Syllabus [pdf version]



Artificial Intelligence Methods for Social Good
Meeting Days, Times, Location: Tue/Thu 10:30am-11:50am, GHC 5222
Semester: Spring, Year: 2018
Units: 9/12, Section(s): 08-537/08-737


Instructor information

Name Dr. Fei Fang
Contact Info Email:
Office location Wean Hall 4126
Office hours Tue/Thu 1pm-2pm


TA Information

TA name Chun Kai Ling
TA Contact Info Email:
Office location GHC 6507
Office hours

Course Description

The rapid advance in artificial intelligence (AI) has opened up new possibilities of using AI to tackle the most challenging societal problems today. This course brings together a set of advanced AI methods that allow us to address such challenges and promote social good:

  • Optimization: mathematical programming, robust optimization, influence maximization
  • Game Theory and Mechanism Design: security games, human behavior modeling, auction and market equilibrium, citizen science
  • Machine Learning: classification, clustering, probabilistic graphical models, deep learning
  • Sequential Decision Making: Markov Decision Processes (MDPs), partially observable MDPs, online planning, reinforcement learning


In addition to providing a deep understanding of these methods, the course will introduce which societal challenges they can tackle and how, in the areas of (i) healthcare, (ii) social welfare, (iii) security and privacy, (iv) environmental sustainability. The course will also cover special topics such as AI and Ethics and AI and Humans. Example research projects and social good outcomes can be found at


The course content is designed to not have too much overlap with other AI courses offered at CMU. Although the course is listed within SCS, it should be of interest to students in several other departments, including ECE, EPP and SDS.


(9 Unit) The students in this 9-unit course are expected to have taken at least three mathematics courses covering linear algebra, calculus, and probability. The students will work in groups on a systematic literature review or a project exploring the possibility of applying existing AI tools to a societal problem, with a survey paper or technical report and presentation delivered at the end of the semester.


(12 Unit) This 12-unit course is only open to graduate students (master and Ph.D. students) with previous programming experience and background knowledge in artificial intelligence. The students will work in groups on a research project with a research-style paper and an oral presentation delivered at the end of the semester. Please see the instructor if you are unsure whether your background is suitable for the course.

Learning Objectives

At the end of the course, the students should be able to

  • Identify societal challenges that can potentially be tackled by AI methods, and determine which AI methods can be applied
  • Describe the AI methods covered in the course, including the basic concepts, the key algorithms, and the commonly-used implementation of the methods
  • Model the societal challenges as mathematical problems that AI techniques can be applied and propose how to adjust and modify the AI techniques to fit the problems
  • Describe evaluation criteria and methodologies of applying AI methods for social good
  • Deliver written and oral presentation on research projects or research survey

Learning Resources

No formal textbook. References and additional resources will be provided in slides and on Canvas.


The final course grade will be calculated using the following categories:


Assessment Percentage of Final Grade
Class participation 10 points
Paper Summaries 20 points
Written Answers Assignment 20 points
Final Project 50 points


  • Class participation. The grading of the class participation will be mostly based on attendance, checked by in-class quizzes and asking and answering questions in class. Other factors include asking and answering questions on Canvas.
  • Paper reading assignment. The course will require all students to complete weekly paper reading assignments individually. In each assignment, the students are required to provide a summary of the paper/article, a list of questions, and a few brainstorming ideas. The assignments will be submitted through Canvas and will be peer reviewed, but the final score will be provided by the instructor and the TA. For each student, the lowest scored assignment will be dropped.
  • Written answers assignment. The course will require all students to complete biweekly written answers assignment individually. Each written answers assignment will involve checking the understanding of basic concepts and working through the algorithms presented in class on example problems. Most questions are multiple-choice questions or numerical answer questions. In addition to providing the answers, the students are required to provide explanations to the answers. The answers will be submitted and auto-graded through Canvas. The explanations will be checked through peer review. For each student, the lowest scored assignment will be dropped.
  • Final project. The students will work in small groups (1-3 students in each group). The students are expected to focus on one or more societal challenges, summarize or propose models and AI-based solutions to tackle the challenges, and evaluate the solutions. The students are required to submit project report through Canvas and deliver oral presentation. The instructor will provide suggested project topics. The students can also propose their own projects topics related to AI and Social Good but they will need consent from the instructor. The progress of projects will be checked through Project Proposal, Project Progress Report, Project Presentation, and Final Project Report. The proposal and progress report will be peer reviewed. The presentation and the final report will be evaluated by instructor and TA directly.
  • The students who choose the 12-unit section will work on a research project, with a research-style paper and an oral presentation delivered at the end of the semester. Ph.D. students may choose a paper format with the guidance of their advisors. For master students, a sample research-style paper can be found here:
  • The students who choose the 9-unit section will work on a systematic literature review or a project exploring the possibility of applying existing AI tools to a social good problem, with a survey paper or technical report and presentation delivered at the end of the semester.

Students will be assigned the following final letter grades, based on calculations coming from the course assessment section.


Grade Percentage Interval
A [90,100] points
B [80,90) points
C [70,80) points
D [60,70) points
R (F) [0,59) points

Grading Policies

  • Late-work policy and Make-up work policy: No late days or make-up work is allowed. However, for both paper reading assignments and written answer assignments, the lowest scored assignment in each category will be dropped.
  • Re-grade policy: To request a re-grade, the student need to write an email to the instructor titled “Re-grade request from [Student’s Full Name]” within one week of receiving the graded assignment.
  • Attendance and participation policy: Attendance and participation will be a graded component of the course. The grading of the class participation will be mostly based on attendance, checked by in-class quizzes and asking and answering questions in class. Other factors include asking and answering questions on Canvas.

Course Policies

  • Academic Integrity & Collaboration: For both paper reading assignments and written answer assignments, a student can discuss with other students, but he need to specify the names of the students he discussed with in the submission, and complete the calculations and writing of explanations, summary, and questions on his own. For the final project, the students can discuss and collaborate with others (including students, faculty members, and domain experts), but the students need to give proper credits to whoever involved, and report the contributions of each group member in the final report and presentations, which will be considered in the grading. For assignments and final project, it is allowed to use publicly available code packages but the source of code package needs to be specified in the submission. Plagiarism is not allowed. The policy is motivated by CMU policy on academic integrity
  • Mobile Devices: Mobile devices are allowed in class. Cellphones should be in silent mode. Students who use tablet in upright position and laptops will be asked to sit in the back rows of the classroom.
  • Accommodations for students with disabilities: If you have a disability and require accommodations, please contact Catherine Getchell, Director of Disability Resources, 412-268-6121, If you have an accommodations letter from the Disability Resources office, I encourage you to discuss your accommodations and needs with me as early in the semester as possible. I will work with you to ensure that accommodations are provided as appropriate.
  • Statement on student wellness: As a student, you may experience a range of challenges that can interfere with learning, such as strained relationships, increased anxiety, substance use, feeling down, difficulty concentrating and/or lack of motivation. These mental health concerns or stressful events may diminish your academic performance and/or reduce your ability to participate in daily activities. CMU services are available, and treatment does work. You can learn more about confidential mental health services available on campus at: Support is always available (24/7) from Counseling and Psychological Services: 412-268-2922.

Course Schedule (Subject to Change)

Date Theme/Topic Assignment Due Reference
1/16 M0: Introduction, Logistics, Course Project  M0 [pdf] HW0 [pdf]
1/18 M1-1 [Optimization]: Optimization Problems

Cover: Convex optimization, Linear Programming (LP) and Mixed Integer Linear Programming (MILP)

Convex Optimization, Chapters 1-4

Stephen Boyd and Lieven Vandenberghe (Cambridge University Press)

Applied Mathematical Programming, Chapters 2, 9

Bradley, Hax, and Magnanti (Addison-Wesley, 1977)

1/23 M1-2 [Optimization]: Conservation Planning

Cover: Wildlife corridor design

PRA1 due (PRA1) Solving Connected Subgraph Problems in Wildlife Conservation

Bistra Dilkina & Carla P. Gomes

Trade-offs and efficiencies in optimal budget-constrained multispecies corridor networks

Bistra Dilkina, Rachel Houtman, Carla P. Gomes, Claire A. Montgomery, Kevin S. McKelvey, Katherine Kendall, Tabitha A. Graves, Richard Bernstein, Michael K. Schwartz

Robust Network Design for Multispecies Conservation

Ronan Le Bras, Bistra Dilkina, Yexiang Xue, Carla P. Gomes, Kevin S. McKelvey, Michael K. Schwartz, Claire A. Montgomery

1/25 M2-1 [Game Theory]: Basics of Game Theory

Cover: Minimax theory, Nash Equilibrium, Stackelberg Equilibrium

HW0 due Algorithmic Game Theory, Chapters 1-3

Editors: Noam Nisan,‎ Tim Roughgarden, Eva Tardos,‎ Vijay V. Vazirani (Cambridge University Press)

1/30 M2-2 [Game Theory]: Security Games

Cover: Ferry protection, ranger patrol planning

PRA2 due


(PRA2) Deployed ARMOR Protection: The Application of a Game Theoretic Model for Security at the Los Angeles International Airport

James Pita, Manish Jain, Janusz Marecki, Fernando Ordóñez, Christopher Portway, Milind Tambe, Craig Western, Praveen Paruchuri, Sarit Kraus

Optimal Patrol Strategy for Protecting Moving Targets with Multiple Mobile Resources

Fei Fang, Albert Xin Jiang, Milind Tambe

Deploying PAWS: Field Optimization of the Protection Assistant for Wildlife Security

Fei Fang, Thanh H. Nguyen, Rob Pickles, Wai Y. Lam, Gopalasamy R. Clements, Bo An, Amandeep Singh, Milind Tambe, Andrew Lemieux

2/1 M2-3 [Game Theory]: Human Behavior Modeling

Cover: Prospect theory, quantal response, cognitive hierarchy

HW1 due

Submit Final Project Group Member List

 (PRA4) Comparing Human Behavior Models in Repeated Stackelberg Security Games: An Extended Study

Debarun Kar, Fei Fang, Francesco M. Delle Fave, Nicole Sintov, Milind Tambe, Arnaud Lyet

Improving Resource Allocation Strategy Against Human Adversaries in Security Games

Rong Yang, Christopher Kiekintveld, Fernando Ordonez, Milind Tambe, Richard John

Predicting human behavior in unrepeated, simultaneous-move games

James R. Wright, Kevin Leyton-Brown

2/6 Guest Lecture by Prof. Illah Nourbakhsh (Carnegie Mellon University)

AI and Ethics

(may move to other dates in Feb)

PRA3 due (PRA3) The Rhetoric of Robotics

Illah Reza Nourbakhsh

2/8 M3-1 [Machine Learning]: Classification and Clustering

Cover: Decision trees, k-means, Gaussian Mixture Models (GMMs)

PRA4 due Pattern Recognition and Machine Learning, Chapters 4, 9

Christopher Bishop

2/13 M3-2 [Machine Learning]:

Probabilistic Graphical Models

Cover: Dynamic Bayesian Networks (DBNs), Markov Random Fields (MRFs)

HW2 due Pattern Recognition and Machine Learning, Chapter 8

Christopher Bishop

2/15 M3-3 [Machine Learning]: Predicting Illegal Activities

Cover: Predict poaching threat, predict urban crime

Project Proposal due (PRA5) Keeping Pace with Criminals: An Extended Study of Designing Patrol Allocation against Adaptive Opportunistic Criminals

Chao Zhang, Shahrzad Gholami, Debarun Kar, Arunesh Sinha, Manish Jain, Ripple Goyal, Milind Tambe

Taking it for a Test Drive: A Hybrid Spatio-temporal Model for Wildlife Poaching Prediction Evaluated through a Controlled Field Test

Shahrzad Gholami, Benjamin Ford, Fei Fang, Andrew Plumptre, Milind Tambe, Margaret Driciru, Fred Wanyama, Aggrey Rwetsiba, Mustapha Nsubaga, Joshua Mabonga

Cloudy with a Chance of Poaching: Adversary Behavior Modeling and Forecasting with Real-World Poaching Data

Debarun Kar, Benjamin Ford, Shahrzad Gholami, Fei Fang, Andrew Plumptre, Milind Tambe, Margaret Driciru, Fred Wanyama, Aggrey Rwetsiba

2/20 M3-4 [Machine Learning]: Regression

Cover: Linear Regression, Regularization, Support Vector Machines (SVMs)

PRA5 due Pattern Recognition and Machine Learning, Chapters 3, 6

Christopher Bishop

2/22 M4-1 [Sequential Decision Making] Markov Decision Process (MDP)

Cover: Value iteration

HW3 due Reinforcement Learning: An Introduction, Chapter 3

Richard S. Sutton and Andrew G. Barto

2/27 M1-3 [Optimization] Combinatorial Optimization and Robust Optimization

Cover: Duality, branch and price, maximin model, minimax regret, prevent illegal fishing

PRA6 due Combinatorial Optimization: Algorithms and Complexity, Chapters 3

Christos H. Papadimitriou,‎ Kenneth Steiglitz

Branch-and-price: Column generation for solving huge integer programs

Cynthia Barnhart, Ellis L. Johnson, George L. Nemhauser, Martin W. P. Savelsbergh, Pamela H. Vance

Robust protection of fisheries with COmPASS

William Haskell, Debarun Kar, Fei Fang, Milind Tambe, Sam Cheung, Elizabeth Denicola

3/1 Guest Lecture by Prof. Tuomas Sandholm (Carnegie Mellon University)

Cover: Kidney exchange

HW4 due (PRA6) FutureMatch: Combining Human Value Judgments and Machine Learning to Match in Dynamic Environments.

Dickerson, J. and Sandholm, T.

In Proceedings of the AAAI Conference on Artificial IntelligenceExtended version with appendix.

Position-Indexed Formulations for Kidney Exchange.

Dickerson, J., Manlove, D., Plaut, B., Sandholm, T., and Trimble J.

In Proceedings of the ACM Conference on Economics and Computation (EC)Extended version.

3/6 M4-2 [Sequential Decision Making]: Partially Observable MDPs

Cover: Online Planning, Monte-Carlo Tree Search (MCTS)

PRA7 due Monte-Carlo Planning in Large POMDPs

David Silver, Joel Veness

Bandit based Monte-Carlo Planning

Levente Kocsis and Csaba Szepesvari

3/8 Guest Lecture by Prof. Norman Sadeh (Carnegie Mellon University)

Cover: Security and privacy, Livehoods: understand city using social media

(PRA7) The Livehoods Project: Utilizing Social Media to Understand the Dynamics of a City

Justin Cranshaw Raz Schwartz Jason I. Hong Norman Sadeh

3/13 Spring Break, no class
3/15 Spring Break, no class
3/20 Guest Lecture by Prof. Phebe Vayanos (University of Southern California)

Cover: Kidneys for translation, housing for homeless youth

PRA8 due

Project Progress Report due

Robust Multiclass Queuing Theory for Wait Time Estimation in Resource Allocation Systems

Chaithanya Bandi, Nikolaos Trichakis, Phebe Vayanos


3/22 Guest Lecture by Prof. Stephen Smith (Carnegie Mellon University)

Cover: Smart traffic light control

HW5 due (PRA8) Accomodating High Value-of-Time Drives in Market-Driven Traffic Signal Control

Isaac Isukapati and Stephen Smith

3/27 M4-3 [Sequential Decision Making]: Invasive Species Management

Cover: Reinforcement Learning, Invasive species management

PRA9 due (PRA9) PAC Optimal MDP Planning with Application to Invasive Species Management

Majid Alkaee Taleghan, Thomas G. Dietterich, Mark Crowley, Kim Hall, H. Jo Albers

PAC Optimal Planning for Invasive Species Management: Improved Exploration for Reinforcement Learning from Simulator-Defined MDPs

Thomas G. Dietterich, Majid Alkaee Taleghan, Mark Crowley

3/29 M1-4 [Optimization]: Influence Maximization

Cover: Influence propagation models, submodular function optimization

Maximizing the spread of influence through a social network

David Kempe, Jon Kleinberg, Éva Tardos

Submodular Functions: Extensions, Distributions, and Algorithms. A Survey

Shaddin Dughmi

Information and Influence Propagation in Social Networks

Wei Chen, Laks V.S. Lakshmanan, Carlos Castillo

4/3 Guest Lecture by Prof. Milind Tambe (University of Southern California)

Cover: AI and Social Work; HIV prevention among homeless youth

PRA10 due (PRA10) Using Social Networks to Aid Homeless Shelters: Dynamic Influence Maximization under Uncertainty

Amulya Yadav, Hau Chan, Albert Jiang, Haifeng Xu, Eric Rice, Milind Tambe

Influence Maximization in the Field: The Arduous Journey From Emerging to Deployed Application

Amulya Yadav, Bryan Wilder, Eric Rice, Robin Petering, Jaih Craddock, Amanda Yoshioka-Maxwell, Mary Hemler, Laura Onasch-Vera, Milind Tambe, Darlene Woo

Uncharted but not Uninfluenced: Influence Maximization with an Uncertain Network

Bryan Wilder, Amulya Yadav, Nicole Immorlica, Eric Rice, Milind Tambe

4/5 M3-5 [Machine Learning]: Deep Learning

Cover: Neural Networks (NNs), Convolutional NN, Faster RCNN, Detecting human and wildlife from UAV videos

HW6 due Deep Learning, Chapter 6, 9

Ian Goodfellow and Yoshua Bengio and Aaron Courville

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun

SPOT Poachers in Action: Augmenting Conservation Drones with Automatic Detection in Near Real Time

Elizabeth Bondi, Fei Fang, Mark Hamilton, Debarun Kar, Donnabell Dmello, Jongmoo Choi, Robert Hannaford, Arvind Iyer, Lucas Joppa, Milind Tambe, Ram Nevatia

4/10 Guest Lecture by Prof. Stefano Ermon (Stanford University)

Cover: Deep learning for developing countries

PRA11 due (PRA11) Combining satellite imagery and machine learning to predict poverty

Neal Jean, Marshall Burke, Michael Xie, Matthew Davis, David B. Lobell, Stefano Ermon

Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data

Jiaxuan You, Xiaocheng Li, Melvin Low, David Lobell, Stefano Ermon

4/12 M2-4 [Game Theory]: Mechanism Design with Money

Cover: Auction, Truthfulness, Price-of-Anarchy

Algorithmic Game Theory, Chapters 9, 11

Editors: Noam Nisan,‎ Tim Roughgarden, Eva Tardos,‎ Vijay V. Vazirani (Cambridge University Press)

4/17 M2-5 [Game Theory]: Spatio-Temporal Pricing in Ridesharing Platforms

Cover: scheduling and pricing, market equilibrium

HW7 due (PRA12) Spatio-Temporal Pricing for Ridesharing Platforms

Hongyao Ma, Fei Fang, David C. Parkes

4/19 CMU’s Carnival, No class PRA12 due  
4/24 Guest Lecture by Prof. David Danks (Carnegie Mellon University)

Cover: AI and Humans

PRA13 due (PRA13-a) Regulating Autonomous Systems: Beyond Standards

David Danks and Alex John London

(PRA13-b) Finding trust and understanding in autonomous technologies

David Danks

4/26 M2-6 [Game Theory]: Citizen Science

Cover: Citizen science

(PRA14) Avicaching: A Two Stage Game for Bias Reduction in Citizen Science

Yexiang Xue, Ian Davies, Daniel Fink, Christopher Wood, Carla P. Gomes

Behavior Identification in Two-stage Games for Incentivizing Citizen Science Exploration

Yexiang Xue, Ian Davies, Daniel Fink, Christopher Wood, Carla P. Gomes

Improving Your Chances: Boosting Citizen Science Discovery

Yexiang Xue, Bistra Dilkina, Theodoros Damoulas, Daniel Fink, Carla P. Gomes and Steve Kelling

5/1 Course Project Presentation 1 PRA14 due  
5/3 Course Project Presentation 2 HW8 due  
5/10 Final Project Report due