What Is Explainable AI?

Could explainable AI be the solution?

First of all, the origin of the issue is transparency. We’ve always valued it, especially when it comes to high stake decisions. When a sector is critical (crimes, loans, health, insurance policies,…) a huge amount of trust is placed upon the decision making.

Do We Need Complete Transparency?

A counterexample of this could be political leaders. We trust them in making critical decisions for us even though they could make mistakes or act in their own interest.

Explainable AI Automation

Automating things has also always been the source of a very controversial debate around accountability. By removing as many humans as possible in very complex systems. We are able to track who is responsible when something goes wrong which has been shown to be very historically challenging for corporate managers.

And it actually worked!

Deep neural networks work very well for tasks like image recognition. But, cede transparency over the decision process. We do know how it works theoretically because we built it. However, we have no idea how it behaves because of the complexity of the model.

Let’s take a step back

If we want something to make critical and vital decisions for us, we want it to be very transparent and explainable. This way we feel comfortable with its purpose and we can more easily trust it.

Today the most common explainable AI methods used are:

The Layer-wise Relevance Propagation (LRP — 2015 (1)). The goal of this method is to detect which input vectors contribute the most to the output. It could help isolate biases for example.

So What are Explainable AI’s Drawbacks?

ll of those methods have one major drawback: they address the problem after the model has been implemented.

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Adam Rida

Adam Rida

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Engineering student in Applied mathematics and Data Science