Forbes October 14, 2019
One of the biggest issues with Artificial Intelligence and Data Science is the integrity of our data. Even if we did all the right things in our models, and our testing, data might conform to some technical standard of “cleanliness,” there might still be biases in our data as well as “common sense” issues. With Big Data, it is difficult to get to a certain granularity of data validity without proper real-world testing. By real-world testing, we mean that when data is being used to make decisions, as consumers, as testers, as programmers, as data scientists, we look at groups of scenarios to see if the decisions made conform to a kind of “common sense” standard. This is when we...
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