Instructions
- This is not role-playing; you're just a student teaching the other students. But you are responsible not just for learning about this yourself but teaching this material to your fellow students! Take your job seriously for their benefit as well.
- This will sometimes require reading multiple papers to get enough context, and even if it says "skim" or "abstract" in the assignment, if you are assigned this topic you obviously have to read them in full and carefully.
- You are being paired up partly so you have someone to work with and bounce ideas off but also so you can debate what you think about the reading, separately from simply completing the assignment. Take some time to step back and reflect about your own take on whatever the topic is.
- You should expect to draw on the readings from previous weeks and feel free to briefly recap relevant material in the presentation to remind the audience if it is helpful (ML concepts, fairness, privacy, etc). The first part of the semester was about building a foundation for reading these and you should reread some of the more abstract/theoretical articles from previous weeks as needed, in the context of the specific topic you are assigned and try to connect it back to the core concepts. That will help in making the ideas concrete both for you and the other students. For example, if your topic is an analysis of fairness issues in a public sector system, the paper "M. Veale, M. Van Kleek, and R. Binns, Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making" that you read in the digital rights + fairness week is something you should probably revisit, as are the basic technical readings of definitions of algorithmic fairness.
- Feel free to Google around to get more context on the topic you are assigned; this is not any kind of "closed book" exam. If you happen to find commentary on your exact topic (e.g., NYPD surveillance systems), do not just copy someone else's analysis — expect to critique their analysis as well or just use it to give you a little context for how other people (who may in some cases actually know less than you already do) may be thinking about it.
- Submit PDF slides to Venesa via email by noon on the day you are presenting so she can combine them before each class to avoid wasting time. I will not grade the slides separately, just the overall presentation.
- If you have any questions about logistics, please contact Venesa.
- You will have 20 minutes overall, but your presentation should be around 10 minutes to allow for class discussion + Q&A after.
March 21
Team #1 (Finance) - Naoto & Connor
S. Trilling, Fair Algorithmic Housing Loans, Aspen Tech Policy Hub, 2020
Consider the other readings in this section complementary.
Team #2 (Workforce) - Shreya & Airin
M. Raghavan, S. Borocas, J. Kleinberg, and K. Levy, Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices, ACM Conference on Fairness, Accountability, and Transparency, 2020
The other readings in this workforce section should be read as background for this, especially the article from the Brookings Institution.
Team #3 (Healthcare) - Rendy & Takayuki
A. Kaushal, R. Altman, and C. Langlotz, Geographic Distribution of US Cohorts Used to Train Deep Learning Algorithms, Journal of the American Medical Association, 2020
March 28
Team #4 (Crime) - Ananya & Camila