How (in)accurate is machine learning?

Executives and others are increasingly using data when assessing business policies, comparing marketing strategies, and making other decisions. In particular, they use machine learning to analyze data--and the results to make decisions. But how much can an executive trust a recommendation generated by machine learning? Recognizing that uncertainty is involved, and could produce expensive mistakes, Chicago Booth's Max Farrell, Tengyuan Liang, and Sanjog Misra have sought to quantify this uncertainty so that decision makers can take it into account. In the past few years, machine-learning methods have come to dominate data analysis in academia and industry.