Course Home Page for Ethical Algorithm Design (CIS 4230/5230) University of Pennsylvania, Spring 2024

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 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

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) 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
Thu Feb 22
Thu 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 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)

Thu Mar 14 MIDTERM EXAM
Last year's midterm
.
Tue Mar 19
Thu Mar 21
Tue Mar 26
Thu Mar 28
Tue Apr 2
Thu Apr 4
Tue Apr 9
Thu Apr 11
Tue Apr 16
Thu 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 10.12 J. 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 23
Thu 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 . .

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) 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 Thu Feb 22 Thu 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 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)
Thu Mar 14	MIDTERM EXAM	Last year's midterm	.
Tue Mar 19 Thu Mar 21 Tue Mar 26 Thu Mar 28 Tue Apr 2 Thu Apr 4 Tue Apr 9 Thu Apr 11 Tue Apr 16 Thu 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 10.12 J. 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 23 Thu 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	.	.