
Phil Britt
Phil Britt is a veteran journalist with more than 45 years of professional writing experience. He content has appeared in national newspapers, magazines and on various websites.
After graduating from Purdue University in 1978 with a degree in Radio-TV, Phil spent three months as a midnight disc jockey in suburban Chicago before moving into print — and now digital journalism forever, building on his experience as sports editor of the Purdue Exponent.
After working two years on the sports desk at the LaPorte Herald-Argus (now the LaPorte County Herald-Dispatch), Phil moved to the sports desk of the Hammond (now Northwest Indiana) Times. Following five years on the sports desk, Phil covered education for three years, then business for three years before moving on to Savings Institutions, the magazine of the U.S. League of Savings Institutions.
Today Phil lives in suburban Chicago with his wife and with their two adult children. He runs a writing business, covering financial services, marketing, business, technology, robotics and a smattering of other subjects that interest him.
Though now a reformed sportswriter, Phil still read the sports (now digital) pages daily, paying particular attention to Chicago and Purdue teams. Though he grew up on the north side, Phil is a diehard White Sox fan, as his wife.
Publications
- destination CRM35 articles
- CMSWire29 articles
- VKTR13 articles
- Speech Technology Magazine12 articles
- The New Stack2 articles
- MortgagePoint1 article
- Enterprise AI World1 article
Writes Most On
- MIT Researchers Develop New Method to Reduce Bias in AI Models11 Mar—VKTRBias in AI is a well-known challenge, with platform developers working on several potential ways to minimize the issue. Bias can occur as developers design an AI model, from underrepresentation of certain groups in underlying data, human annotators bringing their own biases when labeling data, societal biases in training data and more. MIT researchers think that they have found a solution to this challenge, using a technique that identifies and removes specific points in a training dataset...
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