Using arrest and victimization records from the Chicago PD, a machine learning model can predict the risk of being shot in the next 18 months. UMD College of Information Studies Assistant Professor Zubin Jelveh--alongside co-authors Sara B. Heller of the University of Michigan, Benjamin Jakubowski of the Courant Institute of Mathematical Sciences, and Max Kapustin of the Brooks School of Public Policy--recently published a paper on research that supports that shootings are predictable enough to be preventable. Using arrest and victimization records for almost 644,000 people from the Chicago Police Department, the team trained a machine learning model to predict the risk of being shot in the next 18 months. They addressed central concerns about police data and algorithmic bias by predicting shooting victimization rather than arrest, which accurately captures risk differences across demographic groups despite bias in the predictors. Out-of-sample accuracy is strikingly high: of the 500 people with the highest predicted risk, 13 percent are shot within 18 months, a rate 130 times higher than the average Chicagoan.

Peggy and Ramesh Srinivasan, professor, UCLA Dept. of Information Studies, talk about how technology can be fair and more democratic. He says it is important to remember the architecture that the internet was founded was funded by American taxpayers, which means at its inception it was a public sensibility. They also discuss: How every aspect of life increasingly is being mediated by digital technology. The privatization of public life. The new frontier of data and how it has changed companies.   (4/19/22 - 767) IoT, Internet of Things, Peggy Smedley, artificial intelligence, machine learning, big data, digital transformation, cybersecurity, blockchain, 5G, cloud, sustainability, future of work, podcast, Ramesh Srinivasan, UCLA Dept. of Information Studies This episode is available on all major streaming platforms. If you enjoyed this segment, please consider leaving a review on Apple Podcasts.

Clickbait might not lure readers as before – and using artificial intelligence to detect fake news might be much more complex than previously thought, a team of researchers suggest. Clickbait headlines might not be as enticing to readers as once thought, according to a team of researchers. They added that artificial intelligence – AI – may also come up short when it comes to correctly determining whether a headline is clickbait. In a series of studies, the researchers found that clickbait – headlines that often rely on linguistic gimmicks to tempt readers to read further – often did not perform any better and, in some cases, performed worse than traditional headlines. Because fake news is a concern on social media, researchers have explored using AI to systematically identify and block clickbait.