From the course: Data Science Foundations: Fundamentals
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The importance of interpretability in AI
From the course: Data Science Foundations: Fundamentals
The importance of interpretability in AI
- [Narrator] You've done all the work, you've planned the project, you found data, you cleaned and organized the data, you created a model, you validated the model, and you just want to put a bow on it and be done. Well, one thing that you need to consider before you wrap it all up is who is going to make the decision? Who's using the results and the insights that you got from your analysis because you have a couple of choices. One is maybe you're developing something that is for the benefit and use of algorithms. This is, for instance, a recommendation system, which automatically puts something in front of people or a mortgage application system, which processes it immediately while people are still on the website. In that case, the machines are the ones making the decisions, and machines don't need to understand what they're working with. They have the data and if you've set up the algorithm properly, they can just kind of run with it. Also, machines and algorithms can create…
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