To comply with regulation is one of the last reasons someone should think about making artificial intelligence more explainable. Sure, there are benefits — decisions made by a replicable, well-understood process are more trustworthy than those made by a black-box. However, making your models explainable rewards you in many many ways other than regulation. Most importantly, it makes your business managers happy by providing a higher return on investments.

The good news is that there are hacks your team can start working on right now to make things easier. And it should.

Explainable AI is about ROI

One of the challenges in training an artificially intelligent model is that there is no way to make incremental improvements. You run the model and if the accuracy is just below what would be acceptable, there is no way to ‘push’ it from that point. You will have to think about better mapping of your inputs/ outputs, or perhaps pick a more complex model configuration. And then you need to train all over again to see if your improvements made things better. The process is very iterative, and expensive. On the other hand, if there is more transparency and if it is easier to see what is not working, it is also easier to fix it.

Data scientists with scaled AI programs derive another benefit. It is very common to fail when chasing some large ambition. However, if things are explainable, there may be ancillary successes that become valuable. It’s like thinking that memory foam came out of NASA’s Space Shuttle program.

For example, let’s say at a bank a team is trying to answer the question: how much money should they spend in acquiring a customer? One of the considerations would be how much the customer is worth over her lifetime. This is a multi-faceted question with scores of inputs, multiple assumptions, and complex modeling. Today, in most of the cases this question is answered using heuristics and experience. An AI model may or may not work with any success. However, if the data science team used explainable models, in its effort to predict the lifetime value of the customer, they may stumble upon a very successful model for her credit-worthiness.

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