Course Home Page for Ethical Algorithm Design (CIS 4230/5230) University of Pennsylvania, Spring 2024 (original) (raw)
Ethical Algorithm Design CIS 4230/5230
Spring 2024
Tuesdays and Thursdays 10:15 11:45AM ET
Annenberg 110
Instructor:
Prof. Michael Kearns
mkearns@cis.upenn.edu
Teaching Assistants:
Emily Paul (head TA)
empaul@seas.upenn.edu
Elliu Huang
elliuh@seas.upenn.edu
Jihwan (Albert) Park
albertjp@seas.upenn.edu
Simon Roling
rolings@seas.upenn.edu
Anisha Singrodia
singroa@seas.upenn.edu
Here is a list ofoffice hoursfor 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 in2021, 2020and2019.The first formal offering of the course was inSpring 2022,and the most recent version wasSpring 2023.
Here are the lecture videosfrom 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 theSEAS Engineering Ethics Requirementfor 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 |
|---|---|---|---|
| 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) bookThe 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 25Tue Jan 30 | Foundations of Machine Learning | Lecture Notes | |
| Thu Feb 1Tue Feb 6Thu 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 13Thu Feb 15Tue Feb 20Thu Feb 22Thu Feb 29 | Science of Fair ML: Models and Algorithms | Lecture Notes Slides from a related talk | Readings: 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 followingGoogle 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) |
| Thu Mar 14 | MIDTERM EXAM | Last year's midterm | . |
| Tue Mar 19Thu Mar 21Tue Mar 26Thu Mar 28Tue Apr 2Thu Apr 4Tue Apr 9Thu Apr 11Tue Apr 16Thu Apr 18 | Differential Privacy and Related Topics | Lecture Notes | Here is Homework 2,which will be due mid-April. Please monitor the Slack channel for discussion, additions and modifications.Readings: Broken Promises of Privacy: Responding to the Surprising Failure of Anonymization.P. Ohm. (not assigned, but for your perusal) Differentially Private Query Release Through Adaptive Projection.S. Aydore, W. Brown, M. Kearns, K. Kenthapadi, L. Melis, A. Roth, A. Siva. (not assigned, but will be discussed in lecture) Confidence-ranked reconstruction of census microdata from published statistics.T. Dick, C. Dwork, MK, T. Liu, A. Roth, G. Vetri, S. Wu. (not assigned, but will be discussed in lecture) Differential Privacy Overview.Apple. Skim for discussion in 4/18 lecture. How One of Apple's Key Privacy Safeguards Falls Short.Wired magazine. Skim for discussion in 4/18 lecture. Privacy Loss in Apple's Implementation of Differential Privacy on MacOS 10.12J. Tang, A. Korolova, X. Bai, X. Wang, X. Wang. Skim for discussion in 4/18 lecture. Implementing Differential Privacy: Seven Lessons From the 2020 United States Census.M. Hawes. Skim for discussion in 4/18 lecture. See how your community is moving around differently due to COVID-19.Google Covid Mobility Reports. Skim for discussion in 4/18 lecture. |
| Tue April 23Thu April 25 | Ethical Algorithm Design in the Generative Era | Lecture Notes | Here is Homework 3,which will be due towards the end of reading period. Please monitor the Slack channel for discussion, additions and modifications.Readings: AI model disgorgment: Methods and choices.A. Achille, MK, C. Klingenberg, S. Soatto. (required) |
| Mon May 6 | FINAL EXAM | . | . |