Explainable AI may help with regulation, but the real value is its contribution to ROI of your AI projects. This post discusses some hacks! To comply with regulation is one of the last reasons someone should think about making artificial intelligence more explainable.
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.
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.
Enroll now at CETPA, the best Institute in India for Artificial Intelligence Online Training Course and Certification for students & working professionals & avail 50% instant discount.
Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant
In this article, I clarify the various roles of the data scientist, and how data science compares and overlaps with related fields such as machine learning, deep learning, AI, statistics, IoT, operations research, and applied mathematics.
Artificial Intelligence, Machine Learning, and Data Science are amongst a few terms that have become extremely popular amongst professionals in almost all the fields.
Simple explanations of Artificial Intelligence, Machine Learning, and Deep Learning and how they’re all different