You Don’t Have to Sacrifice Machine Learning Precision for Explainability

There are multiple perceptions of what machine learning explainability is depending on where you sit – everything from – “my regression models are explainable” (yes, they are but that isn’t machine learning!), to explaining the data and its weights that are an input to a machine learning algorithm.

Depending on the use case at hand and business requirements, different levels of explainability exist. In Forrester’s, Evoke Trust with Explainable AI, these different levels of explainability are defined in four categories:

• Mostly opaque, with primary responsibility on users
• Partially opaque, with auditability options
• Partially transparent, with user controls to toggle accuracy
• Fully transparent and explainable

In the report, simMachines, Founder and CTO, Dr. Mueller Molina’s approach, based on similarity, is summarized as follows:

“Some vendors position the transparency of their algorithms as a critical strategic differentiator. Arnoldo Müller-Molina, founder and CTO of simMachines, takes a hard line on the topic of transparency versus opacity, advocating an approach in which the model and the underlying data are fully transparent and explainable. Müller-Molina supports this approach not only to defray risk; he believes that if humans and machines are to work together effectively, humans must understand underlying machine logic. Inderpal Bhandari, global chief data officer of IBM, echoes a similar sentiment with regard to the future of AI technology, believing that ultimately the output of AI will need to be fully transparent.”

In general, explainable AI (XAI) continues to be increasingly important for many reasons, and the methods you choose limit or expand explainability.

In the case of similarity, it is at an individual local prediction level, vs. a global explanation of the model itself. That, in and of itself, is a significant difference between methods.

In credit modeling, regulators are used to being given the algorithm with the weights exposed by feature that describe what the algorithm is doing. That is one level of explainability, but it doesn’t directly answer the question an individual consumer may have which is, “why was I denied credit?”  With regression models, basic reason codes are exposed, but with machine learning, at an individual local prediction level, this would be very difficult.

Would a consumer ever ask, “please describe the algorithm and its characteristics that was used in making this decision?” I don’t think so.

For marketing, risk and fraud applications where consumer privacy, ethical uses of data, a consumer’s right to an explanation under GDPR regulations, other regulatory requirements and internal adoption and trust of what the algorithms are deciding, there are many reasons to ensure the right level of explainability is built in.

So now let’s explore how similarity solves this problem.

First, similarity can be used to implement KNN (K Nearest Neighbor), which is a method that retrieves similar objects to the object of a prediction and uses those similar objects as the underlying basis for making a prediction.

Those similar objects also provide the feature set that enables the prediction to be explained. Their attributes and proximity, or distance, to the object being predicted also tell you “Why” the prediction is being made.

KNN is one of the most accurate machine learning methods. However, the historical problem with KNN is its ability to scale. That’s why data scientists have chosen mostly other methods for commercial grade applications.

simMachines founder, Dr. Arnoldo Mueller, conducted breakthrough research to scale similarity. After spending many years at the Kiyoshi Institute in Japan studying similarity as a method for cracking the “black box” problem inherent with other methods, Dr. Mueller invented methods to quickly search for nearest neighbor objects regardless of the number of objects (rows) or features (columns) in a data set. This enables KNN, when implemented with similarity, to scale.

Overcoming the curse of dimensionality issue that creates enormous computational challenges when using Euclidian distance calculations wasn’t a simple task. Metric Learning is a perfect solution to overcome this curse, as we shall see later on.

Part of the IP of simMachines’ method is in its ability to enable similarity to run at massive scale, and to reveal “the Why” behind every prediction. However, Arnoldo did one other thing…enable not just the exposure of the key features associated with a predicted outcome, but also the weighting of those features based on their level of importance to the prediction. This is called dynamic feature weighting which occurs in real-time when a prediction is made.

Dynamic Feature Weighting prioritizes every feature associated with an individual prediction in order of importance. This is unique and powerful as it rank-orders the primary drivers of the predicted outcome based on their weight or frequency of appearance, as well as the proximity of the objects to the predicted object from which they appear.

CHURN EXAMPLE

In the below example, a traditional ML open source algorithm after training will ingest a query object and make a prediction:

Question: Will this customer cancel their mobile phone plan?

Without knowing why someone is going to churn, what do you do? What preventative action should be taken? Do we want to keep this customer in the first place? Is this customer easy or difficult to retain given their reasons for potentially leaving? These questions can’t be answered except based on internal subjective opinions overall about what drives customer behavior.

However, with similarity, we can return the same prediction, and expose in order of importance, the every data feature associated with making this prediction.

In this case, similarity reveals that the churn prediction customer is on a family plan, recently divorced without children in the household and will churn, as other divorced no children customers previously did, because the price of the plan is too high.
Now you can offer this “likely to churn” customer an individual phone plan proactively. In fact, you can group together all of the predictions that have these weighted features in common and treat them with the same preventative offer. This is called Dynamic Predictive Segmentation. This is the value of dynamic feature weighting.

Third, the other ML, “metric learning” is how state of the art precision is achieved.

A library of distance functions is available and the machine selects the best distance function automatically, as part of the model creation and self-validation process. Euclidean distance is commonly used but there are many other distance functions that can create a more accurate comparison of similar objects to the predicted objects.

In essence, the software is able to see more similar objects as closer together and less similar objects as farther apart than standard Euclidean distance functions. This yields much greater precision!