Ethical aspects of Artificial Intelligence, part 2/2: Differential privacy - Datascience.aero

As the second installment in this series of posts, I will touch upon on the topic of privacy in data science and algorithms. In particular, I'm going to discuss a relatively novel concept of privacy called differential privacy that promises, similar to algorithmic fairness, a way of quantifying the privacy of AI algorithms. When we, as humans, talk about privacy, we mostly refer to a desire to not be observed by others. However, what does privacy mean in the context of algorithms that "observe" us by using data that has information on us? In a very general sense, we could say that privacy will be preserved if, after analysis, the algorithm that used our data (e.g. an application on our smartphones) doesn't know anything about us.