Rationale in Predictive Analytics and A.I.

When a machine learning predictive analytics systems makes predictions about something chances are that we have some idea of what drove it to make these predictions. Oftentimes, we even know how confident the system is about each prediction, something that helps us become confident about it too. However, most A.I. systems (including all modern AIs used in data science) fail in giving us any insight as to how they arrived at their results. This is known as the black box problem and it’s one of the most challenging issues of network-based AIs.

Although things are hopeless for this kind of systems due to their inherent complexity and lack of any order behind their predictive magic, it doesn’t mean that all AIs need to be under the same umbrella. Besides, the A.I. space is mostly unexplored even if often seems to be a fully mapped terrain. Without discounting the immense progress that has been made in network-based systems and their potential, let’s explore the possibility of a different kind of A.I. that is more transparent.

Unfortunately, I cannot be very transparent about this matter as the tech is close-source, while the whole framework it is based on is so far beyond conventional data analytics that most people would have a hard time making sense of it all. So, I’ll keep it high-level enough so that everyone can get the gist of it.

The Rationale heuristic is basically a way analyzing a certain similarity metric to its core components, figuring out how much each one of them contributes to the end result. The similarity metric is non-linear and custom-made, with various versions of it to accommodate different data geometries. As for the components, if they are the original features, then we can have a way to directly link the outputs (decisions) with the inputs, for each data point predicted. By examining each input-output relationship and applying some linear transformation to the measures we obtain, we end up with a vector for each data point, whose components add up to 1.

Naturally, the similarity metric needs to be versatile enough to be usable in different scenarios, while also able to handle high dimensionality. In other words, we need a new method that allows us to process high-dimensional data, without having to dumb it down through a series of meta-features (something all network-based AIs do in one way or another). Of course, no-one is stopping you from using this method with meta-features, but then interpretability goes out the window since these features may not have any inherent meaning attached to them. Unless of course you have generated the meta-features yourself and know how everything is connected.

“But wait,” you may say, “how can an A.I. make predictions with just a single layer of abstraction, so as to enable interpretability through the Rationale heuristic?” Well, if we start thinking laterally about it we can also try to make A.I. systems that emulate this kind of thinking, exhibiting a kind of intuition, if you will. So, if we do all that, then we wouldn’t need to ask this question at all and start asking more meaningful questions such as: what constitutes the most useful data for the A.I. and how can I distill the original dataset to provide that? Because the answer to this question would render any other layer-related questions meaningless.

As someone once said, "Knowledge is having the right answer; Intelligence is asking the right question." So, if we are to make truly intelligence systems, we might want to try acting as intelligent beings ourselves...