We correct or confirm that these algorithms are correct when we do the behavior that it predicted. In data science, put simply, the percentage of correct predictions is called entropy. So a programmer should set entropy at a high enough rate to make the thing true, or valid.

There are so many dilemmas with this. So many…I mean, a tremendous amount. But I am only going to focus on two. I am most interested in how racism shows up in these and the types of information bias that occurs and why.

There is some misconception about who is underserved. In short, I think about underserved in education to mean a student does not have robust access to the resources needed for success in education. That does include funding, but there are many more ways that students do not have access to these critical resources.