XAI has been a talk of the town in recent times, looking at the features it provides that gives us an understanding of the inner functioning of the ML models, but why is it necessary to know what’s happening within a model.

Understanding Artificial Intelligence will always be trouble. The sole purpose of building AI systems was to remove the human element from the system and replace them with a machine. The inspiration for building a complex AI system ultimately came from the human mind. Similar to how a random structure of neurons generates intelligence and makes up all the experiences that we have.

This is also called swarm intelligence or emergent intelligence where a group of insignificant and presumably valueless entities comes together to create complex working structures. Such as ants and bees coming together to create a beautiful system of interconnected agents that contribute to the well-being of the entire hive. Similar to ants, our brain incorporates smaller entities called neurons to generate a more complex and collective structure which gives rise to processes such as our consciousness and thought.

AI – The Black Box

Inspired by our brains, computer scientists such as Rosenblatt, in the sixties, suggested various methods for using a network of so-called perceptrons to cause intelligence. The basic reasoning for this was that if a bunch of perceptrons or artificial neurons come together and are interlinked with each other, they can be trained to do a particular task and this might generate a tiny bit of intelligence in this network.

But as the inspiration was taken from the greatest black box, the human brain itself, there was no sure way of knowing how the network actually came up with its output. It just worked like how a brain does. For instance, if a person chooses to eat something, there’s no way to decode how their brain came up with that particular decision as a highly complex neural structure is at work. An average human brain can have billions of neurons with trillions of connections (i.e., synapses) between these neurons.

But even though the brain is so massively complex, we can surely understand how the person came up with the decisions. How? We can simply ask them. We have languages and we can simply have a conversation with the person to understand the reasons behind their thinking.

Also, the brains are evolved in such a way that it is a combination of everything the AI community is working hard on. It is the best NLP model, the best image detection, and a classifier model. It can learn and train itself on so many other things with such limited data. And more than that it can also give out explanations for its reasoning, all of this packed into the same space.

More Research Needed

As the AI space catches up on all of these features, the inability of AI models to explain their findings and act as a literal black box has been the main concern and it is the thing that is holding back AI in multiple important sectors such as healthcare. But although human brains are the most intelligent structures, they still get affected by biases all the time, and similarly, if an AI is trained using biased data it will gain and learn similar biases. Biases in AI-based systems are also the major concern for its vast adoption currently.

All of these problems, perhaps, could be solved if there was a way to open the black box and find the answers or explanations to the outputs generated by an AI model. XAI or Explainable AI does just that by helping us in understanding these biases and creating models which can be tested and verified to get the reasoning behind the decisions that it has given.

This helps us in understanding the predictions generated by the model, how they were processed by the model, and identifying the features which affected the most. This can not only help us in finding human biases affecting the model but can also help us know how reliable and robust the model is. This can truly help us in creating AI systems that humans can understand and trust.

How will this help us humans advance even more further?

Such unbiased AIs can eventually teach us back on how to remove our own biases and create organizations and societies which are open and inclusive. Such trustable and responsible AIs if approved by the regulators in a field can replace the existing experts in that field and will eventually benefit the public in various forms. For example, an AI system trained to detect tumors from X-Ray images, if approved by a health agency when it is easily explainable, can basically replace the oncologists and can create a health system that is much more cost-friendly for a patient but still very effective in detecting cancer.

Now in a similar fashion imagine such upgrades for every disease where we get accurate predictions with almost no cost. And why stop at healthcare. In almost every sector, that has existed since the beginning of human culture, AIs can play an important role, and combined with explainability and responsibility, these models can be made sure that they will be safer and trustable.

As it is always said, Artificial Intelligence has the potential to advance human evolution to the next level. The next level is the extension of mental abilities with the use of these AI systems. AI can help us become freer and independent from the monotonous nature of our life and can help us build societies that are fair and smarter than ever before. It can also help us in establishing world peace and can unite us to solve even bigger problems such as climate change and space exploration.

But this can only happen when we trust these systems and understand them completely. Hence, XAI and RAI cannot be ignored and that is the reason why we really need them. We must truly leverage the power of Explainable AI and make AI trustable, reliable, and more importantly responsible, to put AIs to full use and advancing human civilization.

ANAI – We are doing our part

We have developed an all-in-one solution for developing and deploying ML models within a few clicks. It covers the end-to-end of an ML and Data pipeline and helps businesses build and deploy ML models faster than any other platform. We provide more than 100 data ingestion methods with more than 300 plus unique ML models to build your solutions with.

And we have also included explainability as an important feature into our platform. We cover more than 25 plus model/techniques so that anyone can dive deeper within the model’s workings and find out how the model works. 

Important of XAI, Why we need XAI, Importance of XAI, Why Enterprises Need XAI, Model Performance and XAI

To know more visit www.anai.io or contact us at info@anai.io.