CSCI 599: AI for Social Good
Location: KAP 167
Time: Monday 3 to 620 PM
Instructors: Milind Tambe/Eric Rice
AI for Social Good is a course which is being jointly taught by Dr. Milind Tambe (Viterbi School of Engineering) and Dr. Eric Rice (Suzanne Dworak-Peck School of Social Work). The purpose of the class is to expose doctoral students and advanced masters level students to a new field of research which merges research techniques in artificial intelligence with theory and research contexts from social work. The course is attached to Drs. Tambe and Rices new Center for Artificial Intelligence in Society (CAIS). This course is intended to be trans-disciplinary. It will be a small seminar style course, with limited enrollment. The intention is to have half of the student body from engineering and social work respectively. There is no need for programming experience or social work practice experience per se, however, a deep connection to either social work or computer science is needed. The course will provide an overview of major methods that can advance this new trans-disciplinary work, from both a conceptual level in computer science and a conceptual level in social work. Topics from engineering will include decision theory, sequential planning under uncertainty, and machine learning predictive models. Topics from social work will include human behavior theories that can be modeled by AI (such as social cognitive theory and social network theory) as well as context driven topics, such as homelessness and health care access. The course meets weekly, is a discussion style course and will include an emphasis on group projects which are tackled in trans-disciplinary teams.
PhD students are expected to participate heavily in teaching concepts to the other half of the class. PhD students should team up in exercises. Each class will typically introduce concepts and generate some exercises for the class to be jointly done.
Rice: Introduction to Social work
Brekke, J. S. (2012). Shaping a science of social work. Research on Social Work Practice, 1049731512441263.
Rice, E., Petering, R., Stringfellow, E., & Craddock, J. (2017). Innovations in Community-Based and Interdisciplinary Research. Research on Social Work Practice.
Tambe: Introduction to Artificial Intelligence
100 year study on Artificial Intelligence (AI100):
AI for Social Good workshop videos and workshop report:
Rice/Tambe: An example Social Work + AI project
In both cases, we will introduce what is the history of the field, what are some major schools of thought, what are current trends. We will introduce basics, things taken for granted. For example, experiment design, IRB approval, search
A key topic of this class will be to get students to form teams. Teams must include students from social work and AI; while teaming arrangements could change over time, the initial team will be responsible for delivering potential topics for book chapters by Feb 6.
Holiday (MLK day)
Rice: Introduction to popular opinion leader interventions
Kelly, J. A., St Lawrence, J. S., Diaz, Y. E., Stevenson, L. Y., Hauth, A. C., Brasfield, T. L., ... & Andrew, M. E. (1991). HIV risk behavior reduction following intervention with key opinion leaders of population: an experimental analysis. American Journal of Public Health, 81(2), 168-171.
Valente, T. W. (2012). Network interventions. Science, 337(6090), 49-53.
Schneider, J. A., Zhou, A. N., & Laumann, E. O. (2015). A new HIV prevention network approach: sociometric peer change agent selection. Social Science & Medicine, 125, 192-202.
Whitbeck, L. B., & Hoyt, D. R. (1999). Nowhere to grow: Homeless and runaway adolescents and their families. Transaction Publishers., ch 1.
Milburn, N. G., Rice, E., Rotheram‐Borus, M. J., Mallett, S., Rosenthal, D., Batterham, P., ... & Duan, N. (2009). Adolescents exiting homelessness over two years: The risk amplification and abatement model. Journal of research on adolescence, 19(4), 762-785.
Tambe: Introduction to decision theory, risk averseness, MDP, POMDP (sequential planning under uncertainty), Social influence maximization, POMDP application to social network intervention
Decision Theory handout emailed to students.
Amulya Yadav , Leandro Marcolino, Eric Rice, Robin Petering, Hailey Winetrobe, Harmony Rhoades, Milind Tambe, Heather Carmichael Preventing HIV Spread in Homeless Populations using PSINET In Proceedings of the Annual Conference on Innovative Applications of Artificial Intelligence (IAAI) 2015
Amulya yadav, Hau Chan, Albert Jiang, Haifeng Xu, Eric Rice, Milind Tambe Using Social Networks to Aid Homeless Shelters: Dynamic Influence Maximization Under Uncertainty In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS) 2016
Rice: Introduction to diffusion of innovations theory to motivate social network interventions
Rogers, E. M. (2010). Diffusion of innovations. Simon and Schuster., Ch1, pp 1-33. Elements of Diffusion
Christakis, N. A., & Fowler, J. H. (2013). Social contagion theory: examining dynamic social networks and human behavior. Statistics in medicine, 32(4), 556-577.
Rogers, E. M. (2002). Diffusion of preventive innovations. Addictive behaviors, 27(6), 989-993.
McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual review of sociology, 415-444.
Christakis, N. A., & Fowler, J. H. (2007). The spread of obesity in a large social network over 32 years. New England journal of medicine, 357(4), 370-379.
Tambe: Introduction to security games and network security games; basic game theory, Normal form games, Dominance, Nash equilibrium; Mixed strategy Nash equilibrium, Stackelberg games
Handout: Game Theory
Serious games, T Nguyen, Significance 13 (6), 14-19, 2016
Ariel D. Procaccia Game Theory Is Useful, Except When It Is Not http://www.symposium-magazine.com/game-theory-is-useful-except-when-it-is-not-ariel-d-procaccia/
Thanh H. Nguyen, Debarun Kar, Matthew Brown, Arunesh Sinha, Albert Xin Jiang, Milind Tambe Towards a Science of Security Games New Frontiers of Multidisciplinary Research in STEAM-H (Book chapter) (edited by B Toni), 2016
Benjamin Ford, Matthew Brown, Amulya Yadav, Amandeep Singh, Arunesh Sinha, Biplav Srivastava, Christopher Kiekintveld, and Milind Tambe Protecting the NECTAR of the Ganga River through Game-Theoretic Factory Inspections International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS), 2016
Jan 30 (Monday) Book chapter/paper proposal due
Guest Lecture (AI): Prof. Phebe Vayanos
Guest Lecture (Social Work): Prof. Shinyi Wu
Book Chapter/Paper proposals due
Rice: Introduction to substance abuse prevention
Rice, E., Milburn, N. G., & Monro, W. (2011). Social networking technology, social network composition, and reductions in substance use among homeless adolescents. Prevention Science, 12(1), 80-88.
Najavits, L. M., Weiss, R. D., Shaw, S. R., & Muenz, L. R. (1998). Seeking safety: Outcome of a new cognitive‐behavioral psychotherapy for women with posttraumatic stress disorder and substance dependence. Journal of traumatic stress, 11(3), 437-456.
Barman-Adhikari, A., Rice, E., Winetrobe, H., & Petering, R. (2015). Social network correlates of methamphetamine, heroin, and cocaine use in a sociometric network of homeless youth. Journal of the Society for Social Work and Research, 6(3), 433-457.
Desai, R. A., Harpaz-Rotem, I., Najavits, L. M., & Rosenheck, R. A. (2008). Impact of the seeking safety program on clinical outcomes among homeless female veterans with psychiatric disorders. Psychiatric services, 59(9), 996.
Tambe: Security and game theory, network security games
Jason Tsai, Thanh H. Nguyen, Milind Tambe Security Games for Controlling ContagionConference on Artificial Intelligence (AAAI), 2012
J. Pita, M. Jain, C. Western, P. Paruchuri, J. Marecki, M. Tambe, F. Ordonez, S. Kraus Using game theory for Los Angeles Airport Security In AI Magazine 30(1):43-57, 2009.
B. An, E. Shieh, R. Yang, M. Tambe, C. Baldwin, J. DiRenzo, B. Maule, G. Meyer PROTECT A Deployed Game Theoretic System for Strategic Security Allocation for the United States Coast Guard In AI Magazine, 33(4):96-110, 2012.
C. Kiekintveld, J. Pita, M. Jain, J. Tsai, M. Tambe, F. Ordonez Computing Optimal Randomized Resource Allocations for Massive Security Games In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2009
P. Paruchuri, J. Pearce, J. Marecki, M. Tambe, F. Ordonez, S. Kraus Playing games with security: An Efficient Exact Algorithm for Bayesian Stackelberg Games In Proceedings of the Seventh International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2008
F. Fang, T. Nguyen1, R. Pickles, W.Y. Lam, G.R. Clements2, B. An, A. Singh, B.C. Schwedock, M. Tambe, A. Lemieux PAWS A Deployed Game-Theoretic Application to Combat Poaching In AI Magazine (to appear)
February 17 (Friday) Problem statement/introduction portion of chapters due
Feb 20: Holiday (Presidents day)
Tambe: Introduction to Machine learning, neural nets, decision trees and crime
Handout on machine learning
Eric Potash, Joe Brew, Alexander Loewi, Subhabrata Majumdar, Andrew Reece, Joe Walsh, Eric Rozier, Emile Jorgenson, Raed Mansour, Rayid Ghani. Predictive Modeling for Public Health: Preventing Childhood Lead Poisoning. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Reading: M.J. Paul, R.W. White, E. Horvitz. Diagnoses, Decisions and Outcomes: Web Search as a Decision Support for Cancer. In Proceedings of WWW 2015
Stefano Ermon et al, paper: http://science.sciencemag.org/content/353/6301/790 video: https://www.youtube.com/watch?v=DafZSeIGLNE&feature=youtu.be
Rice: experimental design
To be announced
Rice: Social science approaches to agent-based simulations of disease and other social problems
Axelrod, R. M. (2006). The evolution of cooperation. Basic books. (ch 1)
Additional to be announced
Nowak, M. A. (2006). Five rules for the evolution of cooperation. science, 314(5805), 1560-1563.
Tambe: Agent-based simulations and their applications
M. Tambe, W. L. Johnson, R. Jones, F. Koss, J. E. Laird, P. S. Rosenbloom, and K. Scwhamb. Intelligent agents for interactive simulation environments. AI Magazine, 16(1):1539, 1995.
J.Tsai, N.Fridman, E.Bowring,M.Brown,S.Epstein,G.Kaminka,S.Marsella,A.Ogden,I.Rika, A. Sheel, M. Taylor, X. Wang, A. Zilka, M. Tambe ESCAPES: Evacuation Simulation with Children, Authorities,Parents,Emotions,andSocial Comparison In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS) (Innovative Applications Track), May 2011
J. Tsai, E. Bowring, S. Marsella, M.Tambe Empirical Evaluation of Computational Emotional Contagion Models In Proceedings of the International Conference on Intelligent Virtual Agents(IVA), September 2011
March 10 (Friday) Preliminary results write up for chapters due
March 13: Holiday (Spring Recess)
Rice: Social science approaches to decision-making under uncertainty and social problems driven by uncertainty
Cook, K. S., Cheshire, C., Rice, E. R., & Nakagawa, S. (2013). Social exchange theory. In Handbook of social psychology (pp. 61-88). Springer Netherlands.
Fulginiti, A., Pahwa, R., Frey, L. M., Rice, E., & Brekke, J. S. (2015). What factors influence the decision to share suicidal thoughts? A multilevel social network analysis of disclosure among individuals with serious mental illness. Suicide and Life-Threatening Behavior.
Tambe: Human behavior modeling and emotions
Handout on behavioral game theory
R. Yang, C. Kiekintveld, R. John, F. Ordonez, M. Tambe Improving Resource Allocation Strategy Against Human Adversaries in Security Games In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), July 2011
J. Pita, R. Maheswaran, M. Tambe, S. Kraus A Robust Approach to Addressing Human Adversaries in Security Games In Proceedings of the European Conference on Artificial Intelligence (ECAI), August 2012.
T. Nguyen, R. Yang, A. Azaria, S. Kraus, M. Tambe Analyzing the effectiveness of adversary modeling in security games In Proceedings of the Conference on Artificial Intelligence (AAAI), August 2013.
D. Kar, F. Fang, F.M. Delle Fave, N. Sintov, M. Tambe, A Game of Thrones: When Human Behavior Models Compete in Repeated Stackelberg Security Games In International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May, 2015
Guest Lecture (Social Work): Prof. Daniel Hackman
The ethics of AI for social good and distopian views on AI
Guest Lecture: To be announced
April 3 (Monday) Full first draft of chapters due
Guest Lecture (AI): To be announced
Guest Lecture (Social Work): To be announced
Guest Lecture (AI): To be announced
Guest Lecture (Social Work): To be announced
Final project presentations
Group Assignment: Book Chapter (90% of class grade)
A complete draft, ready for external review of a chapter focussed on applying artificial intelligence techniques to a critical social problem due at the end of the semester. These chapters will be included in a book which Drs. Tambe, Rice and Vayanos are editing. Groups must work collaboratively and include students from both engineering and social work. A statement of author contributions (i.e. who did what) must be turned in with the final draft.
Rough drafts and partial drafts will be due at different points throughout the semester so that we may provide students with constructive criticism along the way.
5% proposal - Due Monday Jan 30
15% motivate the problem - Due Friday February 17
10% prelim results - Due Friday March 10
15% initial full draft - Due Monday April 3
45% final paper - Due Friday May 5
Ideally chapters will include results from simulation models or modeling techniques using existing real world data. These chapters may be aspirational. An actual intervention is not possible in the time frame of one semester, but groups who propose intervention strategies should have preliminary hypothetical models, at a minimum.
Class Participation (10% of Course Grade)
Students active involvement in the class is considered essential to their growth as practitioners. Their presence in class, along with preparation by having read and considered the assignments, and participation in discussion and activities are essential.
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