machine learning redistricting

View and analyse the years of participation, technologies, number of projects, etc of MGGG Redistricting Lab in Google Summer of Code. If you are new to Maptitude we recommend that you watch all of the tutorials from start to finish as they introduce many Maptitude techniques, tools, and procedures. UNC Greensboro, one of the campuses of the University of North Carolina System, will offer a 10-week REU program from May 2 3 - July 29, 202 2 for 9 nationally recruited undergraduate students from mathematical sciences. Partisan gerrymandering poses a threat to democracy. The pessimistic view is that the gerrymandering software will enable legislators to gerrymander with more precision than ever before, to ensure maximum advantages. AI-drawn voting districts could help stamp out gerrymandering. ZillionInfo develops GeoAI technologies that integrate machine learning, statistic modeling and interactive visualization to help clients dig data insights and facilitate better decision making. in the realms of agriculture, environment, health or infrastructure, that are interested in deepening their knowledge of how machine learning (ML) can be useful for the construction of valuable datasets for use in economics research . A total of $18,891 in cash and other prizes was awarded to the winning teams. news. (2020a), we present a new al­ gorithm for sampling redistricting plans from arbitrary distributions. As the process gets underway, a team in the University of Washington Data Science for Social Good (DSSG) You can take a look at my CV or Google Scholar Profile. What's more, policymakers, as well as citizens, can use the same technologies that supercharged gerrymandering to help stop it. Connecting Colorado for Fair Redistricting: A Public Symposium and Call to Action. I am a first year PhD student in Operations Research at the MIT ORC advised by Dimitris Bertsimas studying optimization and its use in science and society. Scrubbing the bias from machine learning systems. news. Like many techniques in machine learning, computational redistricting has the familiar promise of being an impartial arbiter in place of biased or adversarial human decisions. These appointments are awaiting approval from the Board of Regents and will become effective Aug. 15, 2019. Held Saturday, September 19, 2020, 9 AM-4 PM. We discussed the efficiency gap with Eric McGhee, a leading researcher on the topic at the Public Policy Institute of California, to learn more and then decided to create a tool to (1) generate graphics illustrating the efficiency gap . R makes it simple to efficiently implement statistical techniques and thus it is excellent choice for machine learning tasks. The pessimistic view is that the gerrymandering software will enable legislators to gerrymander with more precision than ever before, to ensure maximum advantages. The top team members won $500 apiece. Creating "living" algorithms and machine learning models that can grow and change over time is a huge focus in data science and AI right now. In 2020, Joe Biden beat Donald Trump in the popular vote by roughly 4.5%. I am broadly interested in integer programming and network optimization with applications in political redistricting, machine learning, and quantum computing. . A machine learning model works accurately when the data provided covers precisely the domain for which the model is designed and is structured according to the characteristics of the model. Machine Learning We use machine learning and human editors to ensure news is relevant to a specific news tag, and will provide supporting context where necessary. The "Redrawing the Map on Redistricting 2010: A National Study" white paper and the . Gerrymandering is one of the most insidious methods out there of influencing our political process. My interests include Artificial Intelligence, Evolutionary Computation, Machine Learning, Redistricting, Social Choice, & Game Theory. Redistricting occurs after the completion of the United States Census, which is conducted every 10 years. 3D AI & Machine Learning Apps Big Data Business Intelligence City Planning . Flipped procurement gives city easy access to innovative solutions. This project informs the state-based congressional redistricting process that occurs every 10 years, and is currently underway, to reflect changes in the U.S. Census. The second-place " Link . I am a Computer Science & Political Science Student at the University of Georgia. Maptitude for Redistricting is used by a supermajority of the state legislatures, political parties, and public interest groups. Moreover, the complexity of the districting task may exceed human capacities. Gerrymandering has long been a problem in the process of choosing elected officials globally, but it is especially a problem in the United States, where political redistricting occurs every two years in response to changes in population distribution and . Human-machine collaboration is key, and transparency is . Two years and many late nights later, Saxena completed HemaCam, a machine-learning web app that can employ any smartphone to diagnose sickle cell disease with 95 percent accuracy when combined . ZillionInfo,redistricting software. . Biography. On Sept. 1, two Democratic state lawmakers filed a lawsuit aiming to stop the Republican-controlled legislature from shaping districts for two years. Spring 2021 - Redistricting begins. Three years later after the publication of the Gerrymandering Index white paper, our Cicero product was extended to include more cities, state legislatures and other countries, so we decided to take a fresh look at the process of political redistricting, this time in the context of the 2010 Census.. Fwd: Questions for Redistricting Commission Meeting 1 message Shelynda Capers <scapers@dls . Most Macs purchased in the last several years will have an Intel CPU, and you can use virtualization software such as VMWare Fusion or Parallels Desktop to run Windows, or indeed you can also boot up your machine natively in Windows using Apple's Boot Camp software. More than 450 students from Rice, Texas Southern University, Prairie View A&M University and the University of Houston registered for the event and 47 teams presented projects for judgment. His expert testimony during a lawsuit led to the Pennsylvania supreme court throwing out the old map. Using the Blue Waters supercomputer, we extend our algorithm to the high-performance-computing realm by using MPI to implement an asynchronous inter-process communication framework. One potential solution is using computational models to automate the districting process by optimising objective and open criteria, such as how spatially compact . Sponsored by: Colorado College, the Interdisciplinary Research Institute on the Study of (in)Equality (IRISE) at the University of Denver, and the League of Women . September 2021 Two recent Data Integration papers published in the Journal of Machine Learning Research. 445 days. This contributed article discusses how optimization is the most transparent and fair method of creating political districts. In this article, I will introduce you to more than 180 data science and machine learning projects solved and explained using the Python programming language. It is math, they say, that could help overcome gerrymandering—the practice of drawing legislative districts that favor one party, class or race. DistrictBuilder is a free redistricting tool that empowers the public to draw better maps. Machine learning solutions with an in-house team Here, everything is cut and dried. TimeHorse November 30, 2020 No Comments. Wesley Pegden, professor of mathematical sciences at Carnegie Mellon University, proved that Pennsylvania's congressional map was a partisan gerrymander—in fact, he used math to show if you drew 10,000,000,000 random maps, 9,999,999,996 of them would be fairer than the existing map. Explore Topics. However, optimization is a highly challenging process that seeks the ideal answer to a problem with hundreds of millions of possible solutions. You have to gather a team from scratch and give them everything they need to do their job correctly, starting with the IT infrastructure (and the data platform in the very center of it). Batra is also a research scientist at Facebook AI Research (FAIR). It is primarily designed for statistical computing and graphics. . January 17, 2021 at 7:00 am. After every census, redistricting authorities draw new boundaries for state legislative and congressional districts. My name is Bradley Howard. By: Emily Keller This fall, Congressional leaders will begin the state redistricting process that takes place every ten years to reflect population changes captured by the U.S. Census. Machine learning is helping find genetics of autism Contributing to the rising popularity of computational social science, this dissertation presents new methods grounded in machine learning for solving several important problems in political science. She, along with her research team, have been using machine learning algorithms to sample districting maps from both US and international sources in conjunction with partisan biases in terms of legislative representation to determine what kind of correlations . In Chapter 2, adapted from coauthored work in Fifield et al. The supplement on available redistricting software systems is authored by Blake Esselstyn who is a North Carolina-based geographer, demographer, and redistricting consultant who first began studying geographic information systems (GIS) more than 25 years . His research interests are in machine learning, algorithms, and fairness. founding Director of its Program in Statistics and Machine Learning Dr. Matt Barreto: Professor of Political Science and Chicana/o and Central American Studies, founder of the Latino Policy & Politics Initiative and Voting Rights Project, UCLA; President and Co-Founder of BSP Research, a research and polling firm; previous faculty appointment The process looks something like this: April 01, 2020 - The US Census is conducted. Allow Me to Choose Your Districts . More specifically, his work focuses on producing algorithms with theoretical guarantees that address fairness issues in various topics such as clustering, matching, and redistricting. (2020), we present a new algorithm for sampling redistricting plans from arbitrary . In 2021, the United States performs its decennial process of drawing legislative districts. Can Gerrymandering Be Ended via Machine Learning? Today, more powerful redistricting software incorporating AI and machine learning is available, and it represents a double-edged sword. In this paper, we propose a new algorithm (PAL-ACS) that transforms the ACS problem into an active learning task by introducing pseudo instances. Wendy Tam Cho, a Senior Researcher with the National Center for SuperComputing at the University of Illinois, is hoping to change that. Section 6 applies techniques from statistics and machine learning to the Texas results to show the importance of using detailed electoral data. Keywords: machine learning; bias and fairness in machine learning; data bias; model bias Machine learning is helping find genetics of autism Batra earned his Ph.D. from . Today, more powerful redistricting software incorporating AI and machine learning is available, and it represents a double-edged sword. All with an intuitive interface powered by a decade of redistricting experience. Video links and additional material posted below. This process must inhibit the ill effects from sole reliance on humans as well as overreliance on machines. I, for one, have been very concerned about how this process will go and rather than sit by and let it happen, I . David Thornburgh, president, Committee of Seventy. The future of machine learning in journalism - January 2, 2019 Tagged data analysis data journalism data science elections gerrymandering redistricting Post navigation Louise Matsakis covers Amazon, internet law, and online culture . Hello! May 23-July 29, 2022 Complex Data Analysis Using Statistical and Machine Learning Tools To be held virtually. Machine Learning We use machine learning and human editors to ensure news is relevant to a specific news tag, and will provide supporting context where necessary. A startup has a plan to solve the housing crisis. The legal battle over redistricting started in Texas this year even before lawmakers sat down to draw new state and congressional district maps. 6.036 Introduction to Machine Learning. Scholars, practitioners and advocates will examine the legal and political landscape of redistricting, preview the ongoing process in North Carolina and around the country, and discuss reforms during a conference Sept. 28-29 at Duke. Seyed Esmaeili is a PhD student at the University of Maryland, College Park. As a result several case studies have demonstrated empirically that deployed algorithmic decision making systems do in fact violate standard notions of . Figure 1. A startup has a plan to solve the housing crisis. Since most of the available data is in an unstructured or loosely structured format, annotating this type of data uses the concept of weak supervision in . Redistricting is an application of the graph-partitioning problem that is NP-Hard. Down to block level. • Early April: Keep Commissioner Danny Moore as the redistricting commission chairman. Two of these distinguishes are affiliated with the School of Computational Science and Engineering. Photo by Michael Dziedzic on Unsplash. In 2019, North Carolina's state Senate moved to use a lottery machine to select a handful of maps from 1,000 AI-drawn maps. By Armin Satzger. Maptitude Learning Resources. Gerrymandering and Computational Redistricting. They argued that, under the state constitution, the . Weighted K-Means algorithm for Redistricting in North Carolina. Census Gerrymandering Machine Learning Redistricting Virginia. Republicans currently control the redistricting of 187 congressional seats compared with only 75 for Democrats, according to an analysis by New York University's Brennan Center for Justice. January 03, 2023 - New districts go into effect. These terms represent several closely related areas within the field of artificial intelligence. The goal of this tutorial is to introduce a wide audience interested in algorithmic fairness to the nascent research area of fair clustering.Specifically, we wish to present a variety of fairness notions used in the context of clustering, argue about the necessity of each of those through corresponding applications, discuss the relationships between . tition π0 (e.g., an actual redistricting plan adopted by the state) and transitions from a valid partition π t to another valid partition π t +1 at each iteration t + 1. Eight faculty members affiliated with the Machine Learning Center at Georgia Tech (ML@GT) have received tenure awards or promotions. In this article, we will create a random forest model to solve a typical machine learning problem: churn prediction. "Redistricting and American Democracy" will also give Duke students and the general public an opportunity to learn how redistricting will impact them -- and . The . . Weekly Labs: 4 weekly labs, each with a SERC question and discussion prompt. Batra's research interests lie at the intersection of machine learning, artificial intelligence, computer vision, and natural language processing. My focus the past two years has been on computational political redistricting and probabilistic measures of electoral fairness, joint work with my Cornell advisor and mentor, David Shmoys. Most states hold public hearings, even though half the country lacks laws requiring public input. Thais's postdoctoral research aims to create more targeted machine learning algorithms attuned to specific problems. Collingwood Research provides redistricting, demography, data analysis, survey research, and other contract-based research to local governments, federal and state government agencies, non-profit organizations, campaigns, and civil-rights organizations. The graphics we created above provide information about efficiency gap advantage for each state Congressional delegation where no seat went uncontested. Section 5 applies this protocol to congressional redistricting in Texas, where both Latino and Black residents are numerous enough to require VRA attention. We deploy a scalable evolutionary algorithm to traverse an absolutely immense solutions space to generate more equitable voting districts, strengthening the integrity of the democratic system in the process. I hope you liked this article on more… The Gazette analysis of public comment submissions relied on emerging machine learning technology designed to identify semantic and textual trends within large sets of text, like what some schools now use to check student work for plagiarism. Machine learning refers to a set of computer science techniques that allow computers to discover patterns in the data without being explicitly programmed. In machine learning, active class selection (ACS) algorithms aim to actively select a class and ask the oracle to provide an instance for that class to optimize a classifier's performance while minimizing the number of requests. 91.7 Ann Arbor/Detroit 104.1 Grand Rapids 91.3 Port Huron 89.7 Lansing 91.1 Flint O nce you start consuming machine learning content such as books, articles, video courses, and blog posts, you will often see the terms like artificial intelligence, machine learning, deep learning, big data, and data science being used interchangeably. Check out the work on Integrated PCA by alum Tiffany Tang and on Integrative Clustering by alum Minjie Wang. founding Director of its Program in Statistics and Machine Learning Dr. Matt Barreto: Professor of Political Science and Chicana/o and Central American Studies, founder of the Latino Policy & Politics Initiative and Voting Rights Project, UCLA; President and Co-Founder of BSP Research, a research and polling firm; previous faculty appointment The U.S. Census Bureau has a rich history of using computational tools to learn about populations and the economy. The esoteric world of pure math doesn't usually play much of a role in promoting fairness in the U.S. political system, but Tufts mathematicians Moon Duchin and Mira Bernstein believe that needs to change. All that said, I'm a software engineer, and I can tell you machine learning and game theory would be perfect to solve this problem, respect the VRA, maximize political efficiency, and be compact. And a workshop convening all around the . We design a hybrid optimization metaheuristic algorithm within an MCMC model. David Thornburgh, president, Committee of Seventy. Learning from geometric data. There is a precedent for computational redistricting. Much of decision making is now rendered at least partly through algorithms which were originally designed to optimize an objective such as accuracy or revenue while mostly ignoring the possible un- fairness or harm that could be caused. The patent described a punched card tabulating machine which launched a new industry and the fruitful marriage of statistics and computer engineering—called "machine learning" since the late . Center for Statistics and Machine Learning Princeton University Seminar Talk at Department of Statistical Science Duke University February 5, 2016 Joint work with Benjamin Fi eld, Michael Higgins, and Alexander Tarr Kosuke Imai (Princeton) Automated Redistricting Simulator Duke (Feb. 2016) 1 / 32 AI & Machine Learning Apps Big Data Business Intelligence City Planning Cloud Computing . I hope the commission will consider this route to . The enormity of the problem can be addressed in 2021 because states like Michigan and Virginia are now seriously addressing the . To this end, we have introduced architectures for several species of data, including point clouds, parametric shapes, and meshes. Kurt Cagle. To approximate the bias of the House map for the upcoming decade, we can assess the partisan lean of districts by their 2020 presidential vote. Quan Le and Zach Rewolinski's project "BakerRipley Challenge: Houston Redistricting" won first place in Rice Datathon 2022. Our video tutorials and webinars are a fast and efficient way to get started with Maptitude. As she attended machine learning conferences, Thais realized her interest in creating more intelligent models that can address on-Earth problems like opioid abuse and COVID-19 vaccine distribution. Pick any state, draw your map, and make your voice heard for fair districts. Check out the work on Integrated PCA by alum Tiffany Tang and on Integrative Clustering by alum Minjie Wang. Washington, DC (July 20, 2017) — Aristotle, the leading political intelligence provider in the U.S., and Matchbox.io, provider of machine learning, predictive analytics and artificial intelligence, announced today a partnership to offer to political campaigns what is anticipated to be the most granular analysis of potential voters. Machine learning encompasses these methods, and also includes an additional . Rice computer scientist will explore the theory and design of non-convex optimization algorithms, which are increasingly important for machine learning. Fair Clustering & Unsupervised Learning. Racial Redistricting: Example of Packing a District . In Chapter 2, adapted from coauthored work in Fifield et al. Developing an unsupervised ML method for drawing the voting districts in North Carolina. Another great way to learn how to perform specific tasks . Redistricting automation does not replace human labor; it improves it. Authors: Leslie Kaelbling, Serena Booth, Marion Boulicault, Dheekshita Kumar, Rodrigo Ochigame. Our team has proposed sensible units for learning from geometric data based in theory, just as convolution was a part of image processing before appearing in neural networks. This short article aims to provide a few pointers for fellow students and applied researchers working on topics related to development, e.g. By legally changing the way votes . Redistricting project wins Rice Datathon February 3, 2022 Roles you will be ready to take on (after CS106B) Make better systems yourself! Before we do this, however, we'll first need to introduce the concept of the tipping-point seat. RedistrictR introduces novel techniques and developments to the area of quantitative redistricting. Contributing to the rising popularity of computational social science, this dissertation presents new methods grounded in machine learning for solving several important problems in political science. The program is funded by an NSF grant DMS-1950549. September 2021 Two recent Data Integration papers published in the Journal of Machine Learning Research. The critical goal for AI in governance is to design successful processes for human-machine collaboration. Featured in Fortune, MIT Technology Review, Slate, and more, Batra is an emerging leader in his craft. I will be graduating with degrees in Computer Science and Political Science. . Any of these solutions works very well for running Maptitude for Redistricting. The team created a stakeholder guide to make the computational redistricting tool GerryChain more accessible to citizen groups, activists, and non-partisan map-drawing commissions. Quan Le and Zach Rewolinski's project "BakerRipley Challenge: Houston Redistricting" won first place in Rice Datathon 2022. Be Ended via Machine Learning algorithms attuned machine learning redistricting specific problems in Fortune, MIT Technology,... Addressing the machine learning redistricting 15, 2019 and meshes importance of using detailed electoral Data, MIT Technology Review,,... Fair districts for sampling redistricting plans from arbitrary algorithm to the high-performance-computing realm by MPI... Intelligence, Evolutionary Computation, Machine Learning research of redistricting experience Gerrymandering software will enable legislators to gerrymander more! 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