In the last decade, we have witnessed an exponential growth in both the complexity and the numbers of Machine Learning (ML) techniques. As a consequence, leveraging such methods to solve real-case problems has become difficult for a Data Scientist (DS). Automated Machine Learning (AutoML) tools were devised to alleviate that task, but easily became as complex as the ML techniques themselves. The DS has started to rely on this kind of tools without understanding their functioning, thus loosing the control over the process.

In this vision paper, we propose HAMLET (Human-centric AutoMl via Logic and Argumentation), a framework that would help the DS to redeem her centrality. HAMLET is inspired to the well-known standard process model CRISP-DM. Iteration after iteration, the knowledge is augmented by acquiring more constraints about the problem until a suitable solution is found. HAMLET leverages Logic and Argumentation to merge both constraints and solutions in an uniformed human- and machine-readable medium, not only allowing an easy exploration of the new knowledge at each iteration, but also enforcing its continuous revision via the AutoML tool and the confrontation between the DS and Domain Experts.