[PyMCon Web Series 06] Protecting Voting Rights With PyMC (May 24, 2023) (Todd Hendricks)

Welcome to the 6th event of the PyMCon Web Series! As part of this series, most events will have both an asynchronous component and a live Q&A.

Speaker: Todd Hendricks
Event type: Recorded Talk with Live Q&A
Date: 2023-05-24T20:00:00Z(subscribe here for email updates)
Time: 1pm PST / 8pm UTC / 4pm ET / 4am Berlin
Register for the event: Meetup event or Zoom
Website: PyMCon Events · PyMCon Web Series

NOTE: The event will be recorded. Subscribe to the PyMC YouTube channel for notifications.

Abstract of The Talk:

Voting, elections, and democracy are hot topics. In the United States, one of the most important laws in this domain, the Voting Rights Act of 1965 (VRA), calls for fairness in the design of election systems so that minorities have an equal opportunity to participate. But elections are complicated phenomena. How do we know when that opportunity has been taken away?

This talk describes how that question is answered with a PyMC implementation of a beta-binomial hierarchical model. In a narrower legal context, the qualitative question of opportunity is inferred by the degree to which an electorate is polarized along racial lines. As the thinking goes, if a minority group has drastically different preferences than the majority, then the minority is exposed and vulnerable to partisan actors who might implement policy designed to weaken the political power of that group. Gerrymandering is a popular example.

The model produces parameter estimates that speak directly to this legal question. Designed in the early 2000s, the model has matured to the point that legal doctrine has coalesced around the quality of its estimates; it forms the backbone of a critically important civil rights law. The talk will discuss the Python implementation and how the posterior is interpreted to inform litigation decisions.


Notebook: On Colab
Todd’s Interview:

Todd’s Recording:

About the Speaker:

Todd Hendricks

Todd is a Data Analyst with the Legal Defense Fund, the oldest civil rights organization in America. At LDF, Todd supports federal voting rights litigation targeting discriminatory election systems. He applies PyMC hierarchical models for developing evidence in the diligence process.

Connect with Todd:
      LinkedIn: https://www.linkedin.com/in/todd-hendricks/
      GitHub: tahentx (Todd Hendricks) · GitHub

Connecting with PyMC

      PyMCon Web Series: https://pymcon.com/
      LinkedIn: https://www.linkedin.com/company/pymc/
      Twitter: https://twitter.com/pymc_devs
      YouTube: PyMC Developers - YouTube
      Meetup: PyMC Online Meetup (New York, NY) | Meetup
      Mastodon: PyMC developers (@pymc@bayes.club) - Bayes Club
      GitHub: GitHub - pymc-devs/pymc: Bayesian Modeling in Python

1 Like

I was curious to hear more about the history of how legal theory and the statistical model developed together. Were lawyer able to argue vote dilution cases before this model developed or did the cases only get started once someone worked out the statistical methods? Did the courts have trouble coming to accept the results of this sort of model?

Thanks for the answer Todd! That’s a fascinating history.

I was also hoping to clarify how the model works a little bit. I understand the original data is aggregated but there is trouble with connecting individual votes to individual race dat. Could you try walking us through how the model circumvents this problem one more time?

that’s really helpful todd thanks!