Author’s note: This is the second in a multi-part series talking about current work on demystifying the black box nature of machine learning. Read part one here.
Part 2. The BlackBoxitude of machine learning
In part 1 of this series, I described the various ways people use the term confidence in machine learning. Technologists tend to think of confidence in statistical terms, while laymen often use the term to ask about how trustworthy a machine learning classifier is, in an operational sense.
For most machine learning techniques, unfortunately, the inner workings of the classification algorithm are anything but transparent – even to techies who work them on a daily basis. The complexity of these methods stems from the great number of input variables in play (dozens to thousands), and the subtlety of the interactions between variables, taken as an ensemble.
Some data is easy to classify. Consider the following data set:
The red line divides the data neatly into two categories. A classifier that can do this is a linear classifier (the shape that separates the classes is a line).
But in the most common case, your data points have a messy relationship with each other that can’t be accurately subdivided with a linear classifier, like the following picture:
In this case, you need a nonlinear classifier. When the number of input variables is high, it’s almost a given that you will have a hard time even making a “classification map” like the previous picture, to help you visualize and understand why the classifier is giving the answers it’s giving – and whether you should trust these answers.
All is not lost. Researchers have been making progress on the problem of how to make the results of machine learning more interpretable and usable from a trust perspective. In the next part of this series, I want to describe some exciting current research that addresses this challenge.