By using machine learning algorithms, we are increasingly able to use computers to perform intellectual tasks at a level approaching that of humans. Given that computers cost less than employees, many people are afraid that humans will therefore necessarily lose their jobs to computers. Contrary to this belief, in this article I show that even when a computer can perform a task more economically than a human, careful analysis suggests that humans and computers working together can sometimes yield even better business outcomes than simply replacing one with the other.
Specifically, I show how a classifier with a reject option can increase worker productivity for certain types of tasks, and I show how to construct and tune such a classifier from a simple scoring function by using two thresholds. I begin with a parable featuring the same characters as the one from Part 1 of this Machine Learning Meets Economics series. I recommend reading Part 1 first, as it sets up much of the terminology I use here.
Source: Datacratic MLDB