Ethical Algorithm Design CIS 4230/5230
Spring 2024
Tuesdays and Thursdays 10:15 11:45AM ET
Annenberg 110


Prof. Michael Kearns

Teaching Assistants:

Emily Paul (head TA)

Elliu Huang

Jihwan (Albert) Park

Simon Roling

Anisha Singrodia

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 the most recent version was Spring 2023.

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 should not be viewed as a substitute for mandatory lecture attendance.

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 
 Lecture Notes 
 Readings, Assignments, and Announcements 
Tue Jan 23
Course Introduction and Overview

Lecture Notes

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.

Thu Jan 25
Tue Jan 30
Foundations of Machine Learning

Lecture Notes

Thu Feb 1
Tue Feb 6
Thu Feb 8
Bias in Machine Learning: COMPAS and ProPublica

Lecture Notes

The following readings are required; you should read the two ProPublica pieces before the Feb 1 lecture so we can discuss them then.

ProPublica article on COMPAS

ProPublica analysis

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)

Tue Feb 13
Thu Feb 15
Tue Feb 20
Science of Fair ML: Models and Algorithms

Lecture Notes


Inherent Trade-Offs in the Fair Determination of Risk Scores. J. Kleinberg, S. Mullainathan, M. Raghavan. (First 8 pages required)

The Frontiers of Fairness in Machine Learning. Alexandra Chouldechova, Aaron Roth. (Required)

Please play around with the following Google demo site on fairness and ML. (Required)

Equality of Opportunity in Supervised Learning. M. Hardt, E. Price, N. Srebro. (Intro required, rest optional; this is the post-processing/bolt-on method)

A Reductions Approach to Fair Classification. A. Agarwal, A. Beygelzimer, M. Dudik, J. Langford, H. Wallach. (Intro required, rest optional; this is the in-processing/constrained optimization/game theory method)

An Empirical Study of Rich Subgroup Fairness for Machine Learning. MK, S. Neel, A. Roth, S. Wu. (Intro required, rest optional; this the rich subgroup/preventing fairness gerrymandering method)

An Algorithmic Framework for Bias Bounties. I. Globus-Harris, MK, A. Roth. (Read at least the Intro and Sections 5 and 6)