Advancements in technologies over the years have created explosions in the field of artificial intelligence. Today in this modern computing era, AI is not only redefining jobs, but it’s redefining careers.
AI has some other branches that many are not aware of (except the techies), and explainable AI is one of them.
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In this article, we are going to have a look at the nuts and bolts of explainable AI and why it is needed.
What Is Explainable AI?
At present, AI is becoming a vital part of today’s tech ecosystem, taking a lot of decisions. However, there are few decisions that AI can’t take right away. We all know that AI is used to generate insights from a vast amount of data, and sometimes it fails to present the data in a manner that is easily understandable. This is when explainable AI come into the scenario and make humans understand.
Simply put, explainable AI is an attempt to make a model output explainable in such a way that it can be trusted and easily understood by humans. Unlike the concept of “black box” where the internal workings can’t be seen (even the designers can’t see), explainable AI follows the concept of “right to the explanation” where is a right to be given an explanation for an output.
“It is like an alternative to Black box,” said Damini Gupta, AVP – AI & Fintech at Mphasis NextLabs, at the Rising 2019. “Explainable AI itself means to make the black box models explainable.”
For example, if apply for a credit card and even after entering all the required information, the system denies your request then it is without a doubt is justifiable that you ask for the exact reason.
Another example is, suppose you are presented with an image of two birds that look the same. So, here explainable AI should be able to distinguish the features of both the birds. They both might have the same wings or body structure or something else, but being two different birds, they would definitely have a feature that makes them different. And this is what explainable AI is supposed to spot and present.
Why Explainable AI Is The Need Of The Hour?
One of the reasons that make explainable AI a prime requirement at this AI-driven era is the need to make AI systems free from biases, robust against tinkering or manipulation attempts and easily understandable in their workings and outcomes. And to all the pain-points, explainable AI is the solution.
An example of explainable AI working on a vertical is the credit market. Matthieu Garnier, SVP Data and Analytics at Equifax once in-conversation with us told that explainable AI is driving the credit analytics market. “All industry is using AI and they are trying to produce people behaviour through algorithms,” said Matthieu. “The adoption of explainable AI in credit has increased so much- that is we can give reasons based on outcomes of the models.”
Furthermore, there are a lot of instances when we are provided with a decision and even though it is right, we end up thinking — why did it happen? why did I succeed? We start wondering. But when we talk about explainable AI, it builds AI models with accountability and the ability to describe why a certain decision was made. And the same questions turn into answers such as — I understand why, I know why I succeed, I know when to trust etc. This is the biggest benefit that explainable AI delivers. It makes sure that the inner workings of an AI system are transparent and system owners/admins can understand what is happening.
Bottom Line
Artificial intelligence definitely is very advanced and have done wonders since the get-go. However, we should not follow AI blindly without understanding its reasoning, making it a powerful force. And if we still want to reap the benefits of AI, explainable is the solution.
Many organisation across the globe are working relentlessly to bring explainable AI to the mainstream. However, there are people who still feel that transparency is not something that can be achieved easily. And when it comes it AI becoming transparent, they are even sceptical about the tech’s smartness. And, over the past couple of years, with the increasing complexity in AI systems, scepticism is just getting higher.
