In the standard machine learning pipeline, a learning algorithm processes some data in order to learn an accurate model of the world or a relevant decision making policy. The humans are completely absent from this pipeline, and yet, in reality, they are at every stage of the process: data collection, algorithm design and parametrization, use of the learned model... In this talk, we will see how this presence affects the learning pipeline, and which new challenges it raises.