Much of the recent attention in Machine Learning and AI has focused on Deep Learning. While this is very effective for certain kinds of data, the models created are "black boxes", i.e. you cannot immediately understand what has been learned.
This is a very old subject in Machine Learning. Dr Andy first came across it in the '80s. His solution was to create a Fuzzy Logic Rule Induction algorithm which has been improved and refined ever since.
In fact he created a whole set of algorithms to handle the various kinds of learning. So there is a Genetic Programming algorithm that handles reinforcement learning, otherwise known as "learning with a critic" which can be used wherever some part of a system needs to be optimized. This was incorporated into a product called Darwin and used to create new futures trading strategies for a very large futures trading organization. We also have an Unsupervised learning algorithm and even an Association learning algorithm. All of these create DARL code as the generated model.
As you can see elsewhere on this site, DARL code is very similar to English language type rules, and so very easy to understand.
It's important to understand what your Machine learning algorithms have learned. Imagine, for instance, that you have created an insurance risk model for home owners that rejects households with two adults of the same sex. It's important to understand, and be able to test the effect of removing any implicit biases of this kind. This is something you can only do with Whitebox machine learning models like DARL.