Interviewing Dr. Sam Wang about fighting gerrymandering using tech and data

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What is gerrymandering and how does it affect people’s electoral rights? How can we use technology and data to empower communities to get the representation they deserve? More importantly, why is 2020 important besides the presidential elections, and how can it shape American politics for at least another decade? In this interview with Princeton professor Sam Wang, we explore what gerrymandering means for local communities and voters, why it has become such a big issue in the last decade, and what citizens can do about it. We also discuss how COVID-19 impacted the 2020 electoral and redistricting processes in the United States, as well as what it means for the current election and gerrymandering cycles.

Sam Wang is a unique figure: a neuroscientist by trade, he started analyzing politics in the early 2000s, when he was among the first to aggregate US presidential polls and use statistical methods to analyze them. This work led to the establishment of the Princeton Election Consortium, a blog opened with the mission to provide informed analysis of US national elections by members of the Princeton academic community. In 2012, he recognized new, systematic distortions in representation in the U.S. House. Understanding the causes of these distortions launched his interest in voting rights and led to the creation of the Princeton Gerrymandering Project. The Princeton Gerrymandering Project conducts nonpartisan analysis to understand and eliminate partisan gerrymandering at a state-by-state level.

In its landmark 2019 decision on partisan gerrymandering, the Supreme Court acknowledged the Princeton Gerrymandering Project’s analysis. While approving of the Project’s mathematical analysis, the court decided that there was no legal standard for it to act with regard to gerrymandering, so it barred challenges on a federal level. This decision breathed new air in Prof. Wang’s work with the Princeton Gerrymandering Project, encouraging them to adopt a federalist, state-by-state approach to pursue reforms against partisan gerrymandering, a topic that’s discussed at length in the episode.

The episode is co-hosted by Tiger and Theodor Marcu, a 2020 Princeton graduate who worked closely with Prof. Wang and the Princeton Gerrymandering Project. He and four other undergraduates founded in 2019 to fight gerrymandering across the US with the help of a novel approach: collecting crowdsourced data about communities. Representable allows nonprofits and community members to draw their communities on a map and submit them to an online database that can then be referenced by journalists, nonprofits, redistricting committees, and lawyers in each state. This data can be used to show how proposed or existing district lines may break communities apart, which is a marker of partisan gerrymandering. In the second half of the episode Tiger and Theodor discuss the role that Representable will play in the 2020 redistricting cycle, how crowdsourced data can be used to fight gerrymandering, as well as the role of online platforms in a democracy.

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