We present an argumentation model to explain the optimal treatment plans recommended by a Satisfiability Modulo Theories solver for multimorbid patients. The resulting framework can be queried obtain supporting reasons nodes on path following of schemes. modelling approach is generic and used justifying similar sequences.

A key challenge in the design of AI systems is how to support people understanding them. We address this by positioning explanations everyday life, within ongoing relations between and artificial agents. By reorienting explainability through more-than-human design, we call for a new approach that considers both agents as active participants constructing understandings. To articulate such an app...

Counterfactual explanations are a prominent example of post-hoc interpretability methods in the explainable Artificial Intelligence (AI) research domain. Differently from other explanation methods, they offer possibility to have recourse against unfavourable outcomes computed by machine learning models. However, this paper we show that retraining models over time may invalidate counterfactual t...