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.
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 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.
PyMCon Web Series: https://pymcon.com/
LinkedIn: PyMC | LinkedIn
Meetup: PyMC Online Meetup | Meetup
Mastodon: PyMC developers (@email@example.com) - Bayes Club
GitHub: GitHub - pymc-devs/pymc: Bayesian Modeling and Probabilistic Programming in Python