A schedule of lectures and readings, subject to change, appears below. By default, please read all articles linked before the start of class on the corresponding day. Additional/optional material will be marked as such.
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Module 0: Introduction
Class 1 (Tuesday, Jan 15): Course overview, motivation, and expectations.
Please complete this brief form for admission into the course.
Class 2 (Thursday, Jan 17) Normative Ethics
Ethics, James Fieser
Researcher Looks at Digital Traces to Help Students, Alexis Blue, UA News, 2018
Normative Ethics, Shelly Kagan
“Bhagavad Gita” as Duty and Virtue Ethics, Bina Gupta, Journal of Religious Ethics, 2006
Module 1: Data Collection, Representation, and Privacy
Class 3 (Tuesday, Jan 22) Data Collection
International Ethical Guidelines for Biomedical Research Involving Human Subjects, WHO, 2002
The Internet is Enabling a New Kind of Poorly Paid Hell, Alana Semuels, The Atlantic, 2018
The Belmont Report – Part 3: Basic Ethical Principles and their Application
Bit by Bit: Social Research in the Digital Age, Matthew Salganik, 2017
Class 4 (Thursday, Jan 24) Data Exclusion.
Big Data and its Exclusions, Jonas Lerman, Stanford Law Review, 2013
Gender Shades, Joy Buolamwini and Timnit Gebru, FAT Conference 2018
Amazon Doesn’t Consider the Race of its Customers. Should It?, David Ingold & Spencer Soper, 2016
The Racial Glass Ceiling: Subordination in American Law and Culture, Roy Brooks, 2017
Bring Back the Bodies (Chapter 1 of Data Feminism), Catherine D’Ignazio & Lauren Klein, 2019
Class 5 (Tuesday, Jan 29th) Privacy
A Precautionary Approach to Big Data Privacy, Arvind Narayanan et al, Computers, Privacy & Data Protection, 2015
We Should be Able to Take Facebook to Court, Neema Singh Guliani, NY Times, 2019
The Algorithmic Foundations of Differential Privacy, Cynthia Dwork and Aaron Roth, 2014
Only You, Your Doctor, and Many Other May Know, Latanya Sweeny, 2018NO CLASS (Thursday, Jan 31st).
NO CLASS (Thursday, Jan 31st).
We will schedule a make-up lecture in February. See you at FAT* (livestreamed)!
Class 6 (Tuesday, Feb 5) Managing Data
Datasheets for Data Sets, Timnit Gebru et al., 2018
Raw Data is an Oxymoron (Introduction), Lisa Gitelman, 2013
Take a quick look at these “Dataset Nutrition Labels“
Also: If possible (not required) bring one dataset to class, preferably one you have worked with/on before, and if possible (not required) bring a laptop set up to work on it (e.g., using Jupyter, R, or whatever setup you prefer).
The Dataset Nutrition Label (paper), Sarah Holland et al, 2018
The Numbers Don’t Speak for Themselves (Chapter 5 of Data Feminism), Catherine D’Ignazio & Lauren Klein, 2019
Module 2: What is Machine Bias
Class 7 (Thursday, Feb 7) Definitions of Fairness
Fairness Definitions Explained, Sahil Verma & Julia Rubin, 2018
Fair Prediction with Disparate Impact, Alexandra Chouldechova, 2017
21 Definitions of Fairness and Their Politics, Arvind Narayanan, FAT* Tutorial (Video), 2018
Inherent Trade-Offs in the Fair Determination of Risk Scores, Kleinberg et al, 2016
50 Years of Test (Un)Fairness: Lessons for Machine Learning, Hutchinson and Mitchel, 2019
Class 8 (Tuesday, Feb 12) Inference and Causation
Correlation, Causation and Confusion, Barrowman, 2014
What if Everything Reveals Everything, Ohm and Peppet, excerpt from Big Data is not a Monolith, 2016
Social Data: Biases, Methodological Pitfalls and Ethical Boundaries, Olteanu et al, 2017
Fair Inference on Outcomes, Nabi and Shpitser, 2018
Class 9 (Thursday, Feb 14) Humans In-the-Loop
Human Decisions and Machine Predictions, Kleinberg et al., 2018
Against prediction, Harcourt, 2005
Disparate Interactions, Green and Chen, 2019
Judgment under Uncertainty: Heuristics and Biases, Kahneman & Tversky, 1974
Class 10 (Tuesday, Feb 19) Guest Lecture & Discussion with Tyler Kleykamp, CT Cheif Data Officer
Take a look at ctdata.org and explore what kind of data they have available, how they present it, and what kind of questions they are exploring.
Note: This class really belongs in module 1.
Class 11 (Thursday, Feb 21) Rejecting the Premise
Intervention over Predictions, Barabas et al, 2018
The Seductive Diversion of “Solving” Bias Using Artificial Intelligence, Powles, 2018
Algorithm Aversion: People Erroneously Avoid Algorithms After Seeing Them Err, Dietvorst, Simmons and Massey, 2014.
The Trouble With Quitting Facebook is that We Like Facebook, Koerth-Baker, 2018
MAKE-UP CLASS (Friday, Feb 22nd, 1-2:15pm, WLH 210) Application: Self-Driving Cars
Ethical Aspects of Self-Driving Cars, Tobias Holstein et. al., 2018
Our Driverless Dilemma, Joshua Greene, 2016
Why China will be the First to Adopt Driverless Cars, Michael Wenderoth, 2018
Also: Take a look at the MIT Moral Machine, and “Judge” several scenarios.
Building a Winning Self-Driving Car in Six Months, Keenan Burnett et. al., 2018
Module 3: Solutions to Bias via Algorithmic Fairness?
Class 13 (Tuesday, Feb 26) “Fair” Classification
Attacking Discrimination with Smarter Machine Learning, Martin Wattenberg et. al., 2016
Equality of Opportunity in Supervised Learning, Moritz Hardt et. al., 2016
Take a look at the AI 360 Demo and read their Guidance on Metrics and Mitigation.
To Classify Is Human (Introduction to “Sorting Things Out: Classification and Its Consequences”), Geoffrey C. Bowker and Susan Leigh Star, 2000
Class 14 (Thursday, Feb 28) Generalized Approaches to Fairness
Class 15 (Tuesday, March 5) Guest Lecture with Josh Kalla, Political Science, Yale.
PLEASE SEE THE TWO FILES UPLOADED ON CANVAS
Note: This class really belongs in module 4.
SPECIAL CLASS 16 (Thursday, March 7, 4-5:15pm, AKW 200) Talk by Joshua Kroll (UC Berkeley).
Accountable Algorithms, Joshua Kroll et. al., 2016
The Cyber Conundrum, Joshua Kroll, 2015
Assigned Short Writing: Write 1 paragraph expanding on one aspect of the talk in relation to our discussions in class. Due Friday, March 8th by 5pm via email to email@example.com . (This counts as one of the colloquium responses).
Note: This class really belongs in module 5.
Class 17 (Tuesday, March 26) Beyond Classification: Ranking, Voting, Subset Selection and More
Class 18 (Thursday, March 28) Fairness in Deep Learning
Module 4: Social Implications and Feedback Loops (starting Tuesday, April 2)
- Tues, April 9th – Technical Report Drafts Due by the beginning of class (please submit via email AND bring a printed copy to class). We will give peer feedback in class.
Module 5: Controlling ML Systems (starting Thursday, April 13)
- Thurs, April 25th – Technical Reports Due (please submit via email). End of class, closing remarks, feedback, and discussion.