May 6, 2021
By Sara Zaske, WSU News
PULLMAN, Wash. With state legislatures nationwide preparing for the once-a-decade redrawing of voting districts, a research team has developed a better computational method to help identify improper gerrymandering designed to favor specific candidates or political parties.
In an article in the Harvard Data Science Review, the researchers describe the improved mathematical methodology of an open source tool called GerryChain. The tool can help observers detect gerrymandering in a voting district plan by creating a pool, or ensemble, of alternate maps that also meet legal voting criteria. This map ensemble can show if the proposed plan is an extreme outlier one that is very unusual from the norm of plans generated without bias, and therefore, likely to be drawn with partisan goals in mind.
Open-source mathematical tool detects gerrymandering theiet.org - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from theiet.org Daily Mail and Mail on Sunday newspapers.
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IMAGE: Visualization of sampled county-preserving Virginia Congressional voting districts, created with the ReCom method in Gerrychain. view more
Credit: Daryl DeFord, Washington State University
PULLMAN, Wash. With state legislatures nationwide preparing for the once-a-decade redrawing of voting districts, a research team has developed a better computational method to help identify improper gerrymandering designed to favor specific candidates or political parties.
In an article in the Harvard Data Science Review, the researchers describe the improved mathematical methodology of an open source tool called GerryChain. The tool can help observers detect gerrymandering in a voting district plan by creating a pool, or ensemble, of alternate maps that also meet legal voting criteria. This map ensemble can show if the proposed plan is an extreme outlier one that is very unusual from the norm of plans generated without bias, and therefore, likely to be drawn with p
Why machine learning, not artificial intelligence, is the right way forward for data science on April 5, 2021, 1:59 PM PST Commentary: We like to imagine an AI-driven future, but it s machine learning that will actually help us to progress, argues expert Michael I. Jordan. Image: iStock/Igor Kutyaev
We bandy about the term artificial intelligence, evoking ideas of creative machines anticipating our every whim, though the reality is more banal: For the foreseeable future, computers will not be able to match humans in their ability to reason abstractly about real-world situations. This is from Michael I. Jordan, one of the foremost authorities on AI and machine learning, who wants us to get real about AI.
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CAMBRIDGE, MA December 16, 2020 In 2019, the National Academies of Science, Engineering, and Medicine (NASEM) published a consensus report for the US Congress Reproducibility and Replicability in Science which addressed a major methodological crisis in the sciences: The fact that many experiments and results are difficult or impossible to reproduce. The conversation about this report and this vital topic continues in a special, twelve-article feature in issue 2:4 of the
Growing awareness of the replication crisis has rocked the fields of medicine and psychology, in particular, where famous experiments and influential findings have been cast into doubt. But these issues affect researchers in a wide range of disciplines from economics to particle physics to climate science and addressing them requires an interdisciplinary approach.