• Course Number: 01:198:345
  • Instructor: Amelie Marian
  • Course Type: major (BA and BS) and the minor
  • Semester 1: Fall
  • Semester 2: Spring
  • Credits: 4
  • Description:

    This class introduces students to various types of algorithms used for real-world decisions in a wide range of applications: public policy, health, e-commerce, justice. We will discuss the social impacts of algorithmic decisions, their legal implications, and the need for transparency and accountability in algorithmic decision-making. The class will cover topics such as bias in data and processes, fairness, game theory, and trust, as they relate to algorithms.

    The class will focus on hands-on projects and discussions that will give students an understanding of a participatory design, and include them as stakeholders of the algorithms they are implementing.

    The course will give students a critical understanding of the often-competing goals of efficiency and optimization with those of transparency, accountability and fairness, which are necessary for trust and widespread adoption of automated decision systems. It will teach them to consider all stakeholders of a decision process, as well as the broader impacts of their algorithmic designs.

  • Prerequisite Information:

    The prerequisites are 01:198:210 or 01:198:112 and knowledge of Python, or by permission of instructor.

    - A grade below a "C" in a prerequisite course will not satisfy that prerequisite requirement.