Explainable Artificial Intelligence (XAI) in Python: 3 Powerful Projects You Need to Know

Author(s): Davide Nardini

Originally published on Towards AI.

Unlock the power of Explainable Artificial Intelligence (XAI) in Python with this comprehensive guide!

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Have You Ever Heard of XAI?

XAI stands for Explainable Artificial Intelligence, a research field aimed at making Machine Learning and Deep Learning models more interpretable.

One of the main criticisms of these models is that they often function as black boxes — powerful tools, indeed, but not very transparent or understandable.

And in many cases, this is true: the more complex a model is, the harder it is to interpret. However, difficult to interpret doesn’t mean impossible!

Those who work in this field and understand its workings know very well that, despite their complexity, these algorithms are not inscrutable.

They are the result of mathematical calculations and computer algorithms and are interpretable and understandable.

In this guide, I’ll introduce you to three fascinating XAI projects in Python that help turn these black boxes into white boxes, making them much easier to interpret!

Are you interested in the code?

I recently integrated this guide with FULL PYTHON CODE. You will find it in my Gumroad profile. It is the cheapest Python Full Tutorial that you can find on this topic. Take a look!

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Published via Towards AI