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A Way To Explain How Your AI Model Works

Artificial intelligence can transform any organization. That’s why 37% of companies already use AI, with nine in ten big businesses investing in AI technology. Still, not everyone can appreciate the benefits of AI. Why is that? One of the major hurdles to AI adoption is that people struggle to understand how AI models work. They can see the recommendations but can’t see why they make sense. This is the challenge that explainable AI solves. Explainable artificial intelligence shows how a model arrives at a conclusion. And in this article, we’ll show you why that’s revolutionary. Ready?

What is explainable AI?

Explainable artificial intelligence (or XAI, for short) is a process that helps people understand an AI model’s output. The explanations show how an AI model works, the expected impact, and any potential human biases. Doing so builds trust in the model’s accuracy and fairness. And the transparency encourages AI-powered decision-making. So if you’re planning on putting an AI model into production in your business, consider making it explainable. Because with all the advances in AI, we humans find it increasingly difficult to see how our algorithms draw their conclusions. Explainable AI not only resolves this for us. It helps AI developers check that their systems are working as intended.

Why do we need explainable AI for business?

Artificial intelligence is somewhat of a black box. What we mean by that is you can’t see what’s happening under the hood. 

You feed data in, get a result — and you’re meant to trust that everything worked as expected. Whereas, in reality, people struggle to trust the opaque process. That’s why we need explainable AI, both in business and many other domains.

Explainable AI helps everyday users understand AI models. And that’s crucial if we want more people to use and trust AI.


Explainable artificial intelligence promises to revolutionize how organizations worldwide perceive AI.

In place of distrusting black-box solutions, stakeholders will be able to see precisely why a computer model has suggested a course of action. In turn, they’ll feel confident following a model’s recommendation.

On top of this, developers will be able to constantly optimize algorithms based on real-time feedback, spotting faults or human bias in logic and correcting course. Thanks to all this, we expect more and more businesses to adopt AI over the next twelve months.