Artificial Intelligence (AI) is often called a Black Box because it’s not always clear how the algorithm was able to reach its conclusion. 

But if we want more people to benefit from AI technology, we need to be able to explain how AI makes decisions, and how it interprets a problem. 

We need a new term: Explainable AI. 

Depending on the sector, algorithms that work as a black box can become more problematic. If you are showing shoe recommendations to a potential customer it is probably not an especially controversial subject. But if you are figuring out whether or not someone should be offered a loan then you probably want to know how the algorithm has reached its conclusion. 

It is even more important to understand how AI makes decisions if we consider that the algorithms are written by humans and the developer can inadvertently add their bias to the model, meaning the output will be skewed based on their beliefs, cultural background and experience.

Explainable AI refers to methods and techniques when running AI algorithms that allow the output to be more easily understood by human experts. 

The main benefit of leveraging Explainable AI is that it increases confidence in the algorithm within your organisation. This can create better conditions for faster adoption of AI-based innovation. 

Avora leverages Explainable AI in its root cause analysis algorithms where the user can see all of the identified factors that have influenced a particular metric. 

This gives the user more context and allows them to make decisions faster and with greater confidence.

Explainable AI Root Cause Analysis

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