How Can You Trust AI? | 7wData
This new web series breaks the mold for data science infotainment, captivating the planet with short webisodes that cover the very best of machine learning and predictive analytics.
Watch the second episode of The Dr. Data Show, which answers the question, “How can you trust artificial intelligence?”
About the Dr. Data Show. This new web series breaks the mold for data science infotainment, captivating the planet with short webisodes that cover the very best of machine learning and predictive analytics.
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Please note that viewing the video (above) is recommended, since it includes complementary visuals. Also, certain vocal inflections and gesticulations hold meaning. Some of the intented meaning is lost by reading this transcript rather watching the video.
Welcome to "The Dr. Data Show"! I'm Eric Siegel.
How can you trust AI? That's a pretty important question. As we rely more and more on computers for tasks that are more and more important and complex, how do we know the machines won't screw up?
After all, machines are really taking over. Computers decide -- or at least help decide -- who stays in jail, who gets a loan, who gets hired for a job, which tax refunds are legit, which patient has cancer, and which objects a self-driving car avoids.
Now, when our machines take on such responsibilities, we often call it artificial intelligence -- but that’s a fuzzy term with no agreed definition and can pretty much mean whatever the heck you want it to mean...
Instead, there’s a better word for it, because, in actuality, for automating such challenging decision-making, the particular technology that’s deployed is machine learning: when computers learn from experience. For example, say you’re a bank and you want your computer to help decide which credit card applicants to approve. Suppose that, in recent times, you’ve accumulated the records of 50 thousand cardholders and you've noted which of those ended up defaulting and never paying their balance. You wanna avoid those kinds of cardholders in the future — they are money down the tube.
The list of records — that data — is experience. It’s history from which to learn. So now it’s time for your computer to do what computers do best: number-crunch and optimize. Uncover the patterns and trends so you can classify new incoming credit card requests as well as possible. That pattern discovery process is machine learning.
So the ultimate question is, how can we trust that the machine has discovered something valid, that what it learned will hold true in new situations never before seen? So for example say it finds that credit card holders who are head-of-household, subscribe to sailboat magazines, and go to the dentist are five times more reliable bill-payers than average. I like totally made that example up, but it’s similar to real insights found by banks. So, assume it has learned a bunch of insights like that, like a few dozen patterns like that. The system sees they hold true over the 50 thousand historical examples it's been analyzing.