Bias in data (scientist) = bias in ML

Because data science is mainly about… data and data scientists, surprisingly ;)

The field of machine learning (ML) is an ever-growing trend that aims at creating programs “as intelligent as us humans”. However, the word “intelligence” is, in truth, quite an overstatement — and experts in the domain have admitted that it should not have sticked. To be honest, machine learning allows you to solve very narrow questions pretty well but it does not resemble our human way of thinking. Rather, it is about giving a program the ability to extract patterns from a set of data, in order to approximate a function that can solve the problem at hand.

As I’ve mentioned in previous blog posts about AI, it all relies on this initial set of data (called the “training dataset”). A machine learning algorithm is basically a blank slate, like a small infant that looks at hundreds of examples to learn right from wrong — and to slowly make the connection as to what it means to be right or wrong! This is the training phase during which the algorithm tweaks its thousands of little knobs to test out millions of combinations: the algorithm searches for the combination that gives the correct answer in as many cases as possible when looking at the training dataset examples. Ultimately, the goal of machine learning is to have a…