  The European Union (EU) through the High-Level Expert Group on Artificial
Intelligence (AI-HLEG) and the General Data Protection Regulation (GDPR) has
recently posed an interesting challenge to the eXplainable AI (XAI) community,
by demanding a more user-centred approach to explain Automated Decision-Making
systems (ADMs). Looking at the relevant literature, XAI is currently focused on
producing explainable software and explanations that generally follow an
approach we could term One-Size-Fits-All, that is unable to meet a requirement
of centring on user needs. One of the causes of this limit is the belief that
making things explainable alone is enough to have pragmatic explanations. Thus,
insisting on a clear separation between explainabilty (something that can be
explained) and explanations, we point to explanatorY AI (YAI) as an alternative
and more powerful approach to win the AI-HLEG challenge. YAI builds over XAI
with the goal to collect and organize explainable information, articulating it
into something we called user-centred explanatory discourses. Through the use
of explanatory discourses/narratives we represent the problem of generating
explanations for Automated Decision-Making systems (ADMs) into the
identification of an appropriate path over an explanatory space, allowing
explainees to interactively explore it and produce the explanation best suited
to their needs.
