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Explaining Deep Learning Results: Artificial Intelligence Outputs

This article was written by Alberto Roldan.

I was reading two articles this week on MIT Technology Review about the difficulties of explaining the decision-making of advanced algorithms that uses AI. This explanation is fundamental as our life's become more intertwined in ways that sometimes we do not even realized. From self-driving cars, who's approved for a loan, and personalized medicine the issue of a methodology to explain the outputs of AI is becoming to the forefront due to liability issues. After 20 years implementing advanced algorithms filed and 12 years in the legal profession I have come with a methodology that help explaining deep learning results in such a way that is understood in layman’s terms. Below is how I would create a minimum viable product (MVP) to develop an application to explain AI outputs.

Source for picture: here

To read the rest of the article, with the explanation of AI outputs, click here. For more articles on deep learning, see here.

This explanation is divided in 4 brief parts.

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