Machine learning, artificial intelligence and neural networks have been governing many aspects of everyday life without being noticed, and becoming a more decisive mechanism for crucial aspects. Machine learning based systems are penetrating and being implemented in an increasing number of mediums, from autonomous cars and personalized news feeds to medical diagnostics and crime potential analysis, which affects our culture, perception and the relationship with machines. They reflect us by generating ethical discussions, while learning to function “correctly” through human input. At the same time, it reveals the necessity to reconsider our tools and methods for debugging, repairing and calibrating. Can an artificial intelligence know what’s best for us? Or mislead us caused by the human error in the dataset? How can we test the sanity of artificial intelligence?