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The Trump administration has taken on three ambitious statistical projects: tracking down cases of voter registration fraud, identifying racism in college admissions and developing an algorithm for “extreme vetting” of visa applications.
These would all be very tricky even for a trained professional. I doubt the president’s people are up to the task.
For all its absurdity, the debate over Obamacare has accomplished something positive: It has educated people that insurance is really about risk pooling -- as in you need both healthy and sick people to participate if it’s going to be affordable for the sick.
Some believe that universal government health coverage is the only way to guarantee such risk-sharing. They will be all the more right in the age of big data.
Lots of algorithms go bad unintentionally. Some of them, however, are made to be criminal. Algorithms are formal rules, usually written in computer code, that make predictions on future events based on historical patterns. To train an algorithm you need to provide historical data as well as a definition of success.
This is a guest post, converted from a letter to me, by Derek Osborne, a father of four and active participant in his community with a strong belief that real change happens at the local level. Derek is a data scientist at Intel where he works on a team that utilizes machine learning techniques to optimize the workforce at Intel. Prior to working at Intel, he earned his Ph.D. from the University of Michigan in Biophysics.
I moved to Hillsboro, Oregon four years ago with my wife and three kids after finishing my Ph.D. at the University of Michigan. Like many parents when choosing a home, I checked on the school scores of the nearby elementary schools and there was a large variance in the Zillow school scores that are taken from greatschools.org.
I’ve got a new Bloomberg View column out: A Mathematician’s Secret: We’re Not All Geniuses See all my Bloomberg View columns here.For each certified genius, there are at least a hundred great people who helped achieve such outstanding results. You don’t have to be a genius to become a mathematician. If you find this statement at all surprising, you’re an example of what's wrong with the way our society identifies, encourages and rewards talent.