The intersection of public policies and public data is fertile ground for data scientists to exercise their profession. Two new use cases that have recently emerged in the United States are drafting political constituencies and predicting political unrest.
After each decennial census, all 50 states must redraw political boundaries to ensure that each congressional district has an equal population, as long as this is “practical.” Hundreds of other state and local districts are also redrawn as a result of the 2020 census.
Redistribution is fraught with political dangers, as there is the potential for the new districts to benefit one party or the other. Charges of gerrymandering are never far away, especially when one party is in control. Now, a group of people in Minnesota are taking a new approach to redrawing the lines fairly: letting the algorithms do it.
According to (Minneapolis) Star-Tribune, a dozen mathematicians and data scientists have embarked on a project to enable a computer model to redraw the state’s eight congressional districts. The group, which calls itself Citizen Data Scientists, relies on the priorities set by a judicial commission.
These requirements include avoiding cards that seek to protect, promote, or defeat any incumbent, candidate or political party, according to the story. It also means maintaining voting blocs among minority communities, as well as trying to jointly maintain population centers with a shared economic, cultural or economic heritage.
“The number of suits is astronomical,” said Sam Hirsch, an attorney who helped assemble the group, according to the Star-Tribune article. “Ten years ago the kinds of things we do weren’t known and weren’t possible.
Armed with great computing power, the group scoured millions of maps before settling on One, which is one of five maps the Judicial Commission will be looking at. According to Hirsch, this is a computer-assisted success story.
“The court sets the priorities, but a computer does a better job of analyzing the data and experimenting with different combinations,” Hirsch told the Star-Tribune.
Monitoring political unrest
Researchers are also studying the algorithms’ potential to provide an early warning of potential political unrest in the United States, such as what happened on January 6, 2021. This event, which local law enforcement did not predict , could be the precursor of a more political process. violence in the future.
One of these groups is CoupCast, a University of Central Florida project that uses machine learning techniques such as gradient amplification and deep neural networks to analyze various societal factors to predict the likelihood of strokes. state and electoral violence in dozens of countries every month.
According to an article in Washington post, the folks at CoupCast are considering doing the same type of analysis for the United States, which would be a new use.
“We now have the data – and the ability – to follow a very different path than we did before,” the To post quotes Clayton Besaw from CoupCast. “It is quite clear from the model that we are heading into a time when we are more at risk of sustained political violence. The building blocks are there.
This type of sentiment analysis has become quite common abroad. The company that is now OmniSci started as an MIT CSAIL project to use social media posts to track the Arab Spring in 2011. Other groups seeking to protect political violence, such as PeaceTech Lab, have also started to focus on the United States.
The latest attempts appear to go beyond social media, which some say is not a reliable indicator of looming unrest. Other types of data, such as income inequality, economic disruption, climate change and levels of social trust, could provide a better forecast of a political storm on the horizon, according to the Post article.
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