Academics / Courses / DescriptionsCOMP_SCI 396: Theory of Data and Decisions
VIEW ALL COURSE TIMES AND SESSIONS
Prerequisites
CS 212 (or an equivalent course in probability) in a previous quarterDescription
Predictive algorithms and big data are increasingly being used by firms and policymakers to guide high-stakes decisions, with a range of ethical, social, and policy implications. This course covers theoretical frameworks for thinking through those implications. The course is split into three parts, which model these issues at different scales. The first part of the course starts with the individual decision maker. We cover foundational theories regarding what information is and how it is used in decision problems. The second part of the course considers the interaction between an agent and an algorithm. We cover topics regarding strategic data disclosure and manipulation. The final part of the course considers broader social implications of algorithm design, with an emphasis on recent topics regarding fairness in algorithm decision-making. The objective of the course is not to provide any "answers" regarding the questions raised, but rather to equip students with tools and frameworks that they can use to develop their own analyses of emerging social issues related to big data and algorithms.
- This course fulfills the Tech Elective.
REFERENCE TEXTBOOKS: N/A
REQUIRED TEXTBOOK: N/A
COURSE COORDINATORS: Prof. Annie Liang
COURSE INSTRUCTOR : Prof. Annie Liang (Spring)