Artificial Intelligence Methods for Social Good (Spring 2018)

08-537 (9-unit) and 08-737 (12-unit), Spring 2018

Artificial Intelligence Methods for Social Good


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 and make a meaningful impact. This course brings together a set of advanced AI methods that allow us to address societal challenges and promote social good. In particular, this course will introduce the following AI methods from the standpoint of being an effective user:

1)    Machine Learning: supervised learning, deep learning
2)    Game Theory and Mechanism Design: security games, human behavior modeling, scheduling and pricing, citizen science
3)    Sequential Decision Making: Markov Decision Processes (MDPs), partially observable MDPs
4)    Planning and Optimization: influence maximization, online planning, combinatorial optimization

In addition to providing a deep understanding of these methods, the course will introduce which societal challenges they can tackle, how to build mathematical models for these challenges, how to adjust and modify the AI methods to fit the practical needs, and how to deploy and evaluate AI-based tools in the field. The course will also cover special topics such as AI and Ethics and Safety of AI. The societal challenges discussed in this course cover the following key areas: (i) healthcare, (ii) social welfare, (iii) security and privacy, (iv) environmental sustainability. 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 two mathematics courses covering linear algebra 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.