Trust but verify: Machine learning's magic masks hidden frailties - SiliconANGLE

The idea sounded good in theory: Rather than giving away full-boat scholarships, colleges could optimize their use of scholarship money to attract students willing to pay most of the tuition costs. So instead of offering a $20,000 scholarship to one needy student, they could divide the same amount into four scholarships of $5,000 each and dangle them in front to wealthier students who might otherwise choose a different school. Luring four paying students instead of one nonpayer would create $240,000 in additional tuition revenue over four years. The widely used practice, called "financial aid leveraging," is a perfect application of machine learning, the form of predictive analytics that has taken the business world by storm. But it turned out that the long-term unintended consequence of this leveraging is an imbalance in the student population between economic classes, with wealthier applicants gaining admission at the expense of poorer but equally qualified peers. Machine learning, a branch of artificial intelligence, applies specialized algorithms to large data sets to discover factors that influence outcomes that might be invisible to humans because of the sheer quantity of data involved. Researchers are using machine learning to tackle a wide variety of tasks of unimaginable complexity, such as determining harmful drug interactions by correlating millions of patient medication records or identifying new factors that contribute to equipment failure in factories. Web-scale giants such as Facebook Inc., Google LLC and Microsoft Corp. have stoked the frenzy by releasing robust machine learning frameworks under open-source licenses.