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Explainable AI- The requirement of knowing processes

Explainable AI is the requisite of knowing process beforehand helping to operate in a succession. It helps in gaining the required results in a definite manner.

Explainable AI is gaining popularity among the management and organizations with each passing day. Why is that? Well, as technology grows and makes processes simpler than they already are, the mechanics behind the development of the said technology become more complex. Which means, the user of the tech can now deploy new tools and techniques to simplify the tasks, but when it comes to understanding what is it that makes the technology perform the way it does or why it does whatever it does, users can be left wondering.

Let’s take an example. Now, we know predictive analytics have been blessing the enterprises with predictions and patterns for better decision making, but what if the management wants to understand the process that led to the said prediction? What if the organization wants to know why didn’t the system come up with a different prediction or even the simplest of the questions like how can this particular prediction be trusted?

These questions often remain unanswered due to the dramatic concept of the “black box”.

The black box concept is nothing but the unknown area of AI and ML where even the developers do not know why the machine does a certain thing.

Which brings us to the more comprehensive question, what is Explainable AI?

Now, since there is so much uncertainty about why the AI does these things and how it does it, it’s only fair to have an area of AI where people know the inside mechanics of the processes and their functioning.

Explainable AI is the set of frameworks and tools that helps the users in understanding and decoding the predictions made by the AI ML models.

When a user or a designer knows the functioning of the AI model, they can use it for debugging and improving the way it performs and carries out certain tasks.

But what is the need for Explainable AI?

Machines are getting smarter with each passing minute. Today, there are machines that can enable other machines to perform tasks and advance a system, machines that can train and develop tools and techniques for different processes and operations.
This results directly in the elevation of the risks that come with the power of technology. It is important to know the way technology works and be able to control its behavior.

With Explainable AI, users can interpret the functionality of their AI-powered tools and use the same to fill any gaps and realign with the areas where the system seemed to have drifted away from. 

Importance of Explainable AI

The market will never run short of competition 

  • Model Accountability is one of the most important things that a company needs to be aware of when implementing AI and ML, and Explainable AI helps with gaining this accountability in the procedures.
  • When a model behaves differently, going over its performance can help organizations troubleshoot the cause of the problem and fix what’s wrong, all the while making improvements in the performance.
  • AI can sometimes be biased, and with Explainable AI and Machine Learning, designers and users can make these models fair and remove the bias from the system by evaluating its functionality.
  • Gaining the trust of the users is highly essential when deploying any technology and with modern day solutions, interpretation can be quite difficult. Explainable AI helps users in comprehending the models, which in turn helps in gauging the trust of the users.
  • Governance and predictive models are two things that should belong next to each other. Explainable AI helps in monitoring the predictive models which makes it possible to streamline its governance.

Explanation for what goes on behind the scenes and a complete guide to how it benefits the users, is the need of the hour. It has been a challenge for many organizations to trust AI models without knowing how it is that they arrive at a decision and Explainable AI is here to change that. Transparency and evaluation are assisting Explainable AI tools to establish an ethical footprint in the market that makes it further more reliable and dependable.
Companies are now deploying Explainable AI to know their technology inside out and enhance the way they perform their tasks and functions.

For more such updates and perspectives around Digital Innovation, IoT, Data Infrastructure, AI & Cybersecurity, go to AI-Techpark.com.

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