Black-box & White-box models

Nowadays, the importance of Artificial intelligence (AI) models in data-driven companies is increasing exponentially. These models are constantly learning and evolving automatically thanks to machine learning. Even though they are very accurate, in most of the cases we will not be able to understand the reasoning behind the model’s predictions. In consequence, one of the biggest challenges that these companies are facing is understanding how their AI models make predictions.

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First of all, we should understand the concepts of white-box and black-box in the Artificial Intelligence field. 

 

White-box models refer to any AI model that can be easily understood by everyone, usually because it has been built using an interpretable architecture such as Linear Regression. The main problem with these architectures is that they don’t scale to large datasets and complex problems such as the ones that companies encounter in daily life. 

 

On the other hand, black-box models are built using complex algorithms that allow them to scale to complex problems. Therefore, even if you are an expert, it is impossible to know how a certain model is actually making its predictions. This is, we are not able to access the reasoning behind their decision-making. That’s why they are called black-boxes, because we cannot look at what happens “inside them” 

 

As we develop machine learning models that make crucial decision, it is a priority to understand their reasoning. As a company, you may want to improve your models by erasing possible biases, or understanding which is the role each variable play. Yet you would require a significant knowledge to do so. 

 

At the end, all comes down to trust. You must trust that models are making the right decisions under the correct assumptions. However, it is hard to have confidence on a system which you do not comprehend. 

 

How do we achieve this confidence in our models? By using Explainable AI to convert those complex black-boxes models into transparent and interpretable white-boxes models.   

 

Since implementing Explainable AI requires expertise and is time consuming, EXPAI has built an efficient solution for all the companies which are looking forward to improve their AI models. We offer our clients the opportunity to improve their AI models by specialized insights and visualizations through an intuitive platform. Our clients can fully understand their models and provide them with a more comprehensive AI that boost their trust and profits. Check out our web section Explainable AI to know more: Click here!

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