Data Science is gonna save the world, right? Or is it? Machine Learning epic fails are being largely commented. It's easy to convince ourselves that they are due to the inconsiderate misuse of Data Science. But is it really so? Is it possible that innocuous choices lead an honnest team to a disaster? During the course of this talk, we will build together an (imaginary) application: a disruptive AI-based smart virtual assistant, pledging to help high-schoolers with their university choice. We will see how unintended biaises may creep in at every step, even with the best of intentions. We will explore different topics, such as algorithmic fairness, model interpretability and the handling of minority classes. Through this practical example, this talk will present a review of major ethical pitfalls identified in the Machine Learning community along with suggestions on how to avoid them. This talk is intended for beginner to intermediate Data Scientists, and people working with Data Scientists, even without specific technical knowledge.