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MS&E 298

Tentative course schedule

Topics subject to change based on course progression.

Week 1 🚔

September 27, 2023

Lecture: Stop-and-frisk. Stop rates. Frisk rates. Hit rates. Benchmark problem. Outcome test.

Link to Week 1 slides

Lecture recording is available on Canvas.

Lab: Data manipulation and plotting.

Link to Week 1 lab

Week 1 lab solutions

Paper discussion: None.

Guest speaker: None.

Homework (submit on Canvas before next class):

  • If you are enrolled, or plan to enroll, complete the Enrolled Student Survey.
  • (optional) Finish and submit the Week 1 lab. Make sure to save a copy of the Colab Notebook to your Google Drive with your answers.
  • Make one slide about one or more topics from the Week 1 lecture. The slide should have an accompanying script corresponding to no more than one minute of talking. Read the Guide to Slide and Scripts for more info and tips.
  • Read the intro, 2.1, 3.2, and 3.3 of the Stop-and-frisk paper.
  • Make one slide about one or more topics from the paper. As above, the slide should have an accompanying short script.

Week 2 ⚖️

October 4, 2023

Lecture: Algorithmic fairness. ProPublica investigation. Differences in error rates. Inframarginality. Threshold test.

Link to Week 2 slides

Lecture recording is available on Canvas.

Lab: Inframarginality.

Link to Week 2 lab

Week 2 lab solutions

Paper discussion: Stop-and-frisk paper.

Guest speaker: None.

Homework (due by next class):

  • (optional) Finish and submit the Week 2 lab
  • Slide+script for Week 2 lecture. If you haven’t already, make sure to read the Guide to Slide and Scripts.
  • Slide+script for the Open Policing Project paper. You don’t have to read the methods, and focus on understanding the figures!
  • Revise Week 1 slides based on feedback and re-submit.

Week 3 👩‍⚖️

October 11, 2023

Lecture: Conceptions of discrimination. Disparate treatment. Omitted variable bias. Ignorability. Post-treatment bias.

Link to Week 3 slides

Lecture recording is available on Canvas.

Lab: Assessing potential discrimination with linear regression.

Link to Week 3 lab

Week 3 lab solutions

Paper discussion: Open Policing Project paper

Guest speaker: Tania Amarillas, Investigator at the Prison Law Office

Tania’s presentation is included in the lecture slides.

Homework (due by next class):

Week 4 🧮

October 18, 2023

Lecture: Disparate impact. Griggs v. Duke Power Co. (1971). Business justification. Risk-adjusted regression. Sensitivity analysis. Threshold rules.

Link to Week 4 slides

Paper discussion: Background on the Public Safety Assessment and Section 2 of Grossman, Nyarko, Goel (2023).

Guest speaker: None.

Homework (due by next class):

Week 5 🤖

October 25, 2023

Lecture: Perceptions of algorithmic decision making. Risk scores. Calibration. Equalized odds. Race and gender in risk scores. Label bias. Feature bias. Sample bias.

Link to Week 5 slides

Paper discussion: None. Reading assigned last week will be covered next week.

Guest speaker: Alex Chohlas-Wood, Executive Director of the Computational Policy Lab

Alex’s slides are posted on Canvas.

Homework (due by next class):

Week 6 🔀

November 1, 2023

Lecture: Discrimination and economics. Taste-based discrimination. Statistical discrimination. Causal effect of race. Randomizing perceptions of race. Natural experiments.

Link to Week 6 slides

Paper discussion: Bertrand and Mullainathan (2004)

Guest speaker: None.

Homework (due by next class):

  • Slide+script for Week 6 lecture
  • Slide+script for Mahari and Lera (2023). Read the entire paper, but focus most on understanding Figure 2(a) and the sections “Observing Bias in Judicial Decisions” and “Prediction with Biographic Information”. No need to read the Methods section. Author Robert Mahari is our guest speaker next week!
  • Revise Week 5 slides based on feedback and re-submit.

Week 7 🔀

November 8, 2023

Lecture: More on economics and discrimination.

Link to Week 7 slides

Paper discussion: Mahari and Lera (2023)

Guest speaker: Robert Mahari, JD-PhD student at Harvard Law School and the MIT Media Lab

Homework (due by next class):

  • Slide+script for Koenecke et al. (2020). Prof. Koenecke is our guest speaker next week!
  • Slide+script for Week 7 lecture
  • Revise Week 5+6 slides based on feedback and re-submit. Week 5 slides were graded later than expected!

Week 8 📱

November 15, 2023

Lecture: Bias in technology. Facial recognition. Audio transcription. Affirmative action in college admissions. Legal challenges. Alleged Asian penalty. Legacy and geographic preferences.

Link to Week 8 slides

Paper discussion: Koenecke et al. (2020)

Guest speaker: Allison Koenecke, Assistant Professor at the Cornell University Department of Information Science

Link to Prof. Koenecke’s slides

Homework (due by next class):

  • Slide+script for Week 8 lecture
  • Slide+script for Coots et al. (2023). Madison Coots is our guest speaker for Week 9!
  • Revise Week 7 slides based on feedback and re-submit.

Thanksgiving week (no class) 🦃

November 22, 2023

Week 9 🏥

November 29, 2023

Lecture: Discrimination in healthcare. Healthcare risk scores. Label bias. Calibration. More on alleged Asian penalty in college admissions.

Link to Week 9 slides

Madison’s slides are included in the Week 9 slides.

Paper discussion: Coots et al. (2023)

Guest speaker: Madison Coots, Ph.D. student in Public Policy at the Harvard Kennedy School of Government

Homework (due by next class):

  • Slide+script for Week 9 lecture OR alternative assignment. See Ed for details.
  • Slide+script for Goldin (2014). Focus on the figures and tables. You can skip Section III.
  • Revise Week 8 slides based on feedback and re-submit.

Week 10 🏫

December 6, 2023

Lecture: Sex and gender discrimination.

Link to Week 10 slides

Paper discussion: Goldin (2014)

Guest speaker: Atticus Ballesteros, Law Clerk at the U.S. Court of Appeals for the Ninth Circuit, formerly ACLU

Homework (due by beginning of finals week):

  • None! Enjoy your winter break.