Ethical Algorithm Design
CIS 4230/5230
Spring 2025
Tuesdays and Thursdays 10:15 11:45AM ET
Annenberg 110
Instructor:
Prof. Michael Kearns
mkearns@cis.upenn.edu
Teaching Assistants:
Elliu Huang
elliuh@seas.upenn.edu
Alexandra Oh
alexoh@seas.upenn.edu
Simon Roling (head TA)
rolings@seas.upenn.edu
Arnab Sircar
asircar@seas.upenn.edu
Here is a list of office hours for all course personnel. You may also request office hours by appointment.
Course Description
This course is about the social and human problems that can arise from algorithms, AI and machine learning, and how we might design these technologies to be "better behaved" in the first place. It is first and foremost a science or engineering course, since we will be developing algorithm design principles. You can get a rough sense of course themes and topics by visiting the websites for the pilot versions of this course offered in 2021, 2020 and 2019. The first formal offering of the course was in Spring 2022, and you can also visit the sites for Spring 2023 and Spring 2024.
Here are the lecture videos from the last pilot version in 2021. Please note that they will not correspond exactly to this year's lectures (and we will cover material not in the videos at all), and should not be viewed as a substitute for mandatory lecture attendance, but rather as a study aid.
Prerequisites: Familiarity with some machine learning, basic statistics and probability theory will be helpful. While this is not a theory class, you need to be comfortable with mathematical notation and formalism. There will be some simple coding and data analysis assignments, so some basic programming ability is needed.
Course content will include readings from the scientifc literature, the mainstream media and other articles and books.
Grades will be based on a mixture of quizzes, coding assignments, written homeworks, and a written midterm and final.
CIS 423/523 fulfills the SEAS Engineering Ethics Requirement for these programs: ASCS, BE, CMPE, CSCI, DMD and NETS (but you should confirm with your academic adivsor to be certain).
Lecture Dates |
Topic |
Lecture Notes |
Readings, Assignments, and Announcements |
|---|---|---|---|
|
Thu Jan 16 |
Course Introduction and Overview |
While they look ahead to material later in the semester, the following two (required) general-audience
articles on the science of Responsible AI are a good preview of the spirit of the class, please read
them in the first week of class or so:
Responsible AI in the generative era, M. Kearns, Amazon Science blog, May 2023. Responsible AI in the wild: lessons learned at AWS, M. Kearns and A. Roth, Amazon Science blog, November 2023. A general-audience introduction to some of the themes of the course is given in the (recommended but not required) book The Ethical Algorithm: The Science of Socially Aware Algorithm Design, by M. Kearns and A. Roth. Also recommended but not required: Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, by C. O'Neil. |
|
|
Tue Jan 21 Thu Jan 33 |
Foundations of Machine Learning |
|
. |
|
Tue Jan 28 Thu Jan 30 Tue Feb 4 |
Bias in Machine Learning: COMPAS and ProPublica |
The following readings are required: Practitioner's Guide to COMPAS Core (no need to read, but we'll peruse a bit together in lecture) COMPAS Risk Assesment Survey (just skim) Northpointe response to ProPublica ProPublica github repository, including dataset (we'll look at the dataset a bit in lecture)(technical, just skim) |