Artificial Intelligence Models
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Introduction
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Artificial Intelligence is becoming a ubiquitous presence in our everyday lives. Supporting and expanding the cognitive abilities of humans or even replacing them, powerful algorithms along with huge amounts of data can now perform a wide variety of tasks, from labeling images to predicting cancer, as well as or even better than a human would do. As AI models grew in processing power and accuracy, they also became more complex, and their predictions, as accurate as they may be, don’t bring with them a clear explanation on how they were achieved. Such operation paradigm may bring drawbacks with it because, as it was recently reinforced [@kuang2017can], AI must “conform to the society we’ve built – one in which decisions require explanations, whether in a court of law, in the way a business is run or in the advice our doctors give us”. This leads us to the need for moving from simply accepting *what a model does*, to interpreting it to understand *how it does* so.

Interpreting the model behavior is not always necessary. Users probably won’t want further explanation about the outputs of systems performing voice recognition or image classification, as long as 