AI systems are becoming more and more the norm as machine and deep learning gain ground -- especially within the data center and colocation markets. That said, artificial intelligence systems are only as good as their underlying mathematics and the data they are trained on. That's according to a new white paper from Alegion that explores the bias behind machine learning. AI systems and models are made up of algorithms and data, and the professionals who craft the models, etc., are largely in charge of underlying mathematics and data. According to the new Alegion white paper, when things go wrong with AI it's for one of two reasons: The Alegion report contends there are four different types of machine learning or AI systems bias.