  This study presents insights gathered from surveys and discussions with
specialists in three domains, aiming to find essential elements for a universal
explanation framework that could be applied to these and other similar use
cases. The insights are incorporated into a software tool that utilizes GP
algorithms, known for their interpretability. The applications analyzed include
a medical scenario (involving predictive ML), a retail use case (involving
prescriptive ML), and an energy use case (also involving predictive ML). We
interviewed professionals from each sector, transcribing their conversations
for further analysis. Additionally, experts and non-experts in these fields
filled out questionnaires designed to probe various dimensions of explanatory
methods. The findings indicate a universal preference for sacrificing a degree
of accuracy in favor of greater explainability. Additionally, we highlight the
significance of feature importance and counterfactual explanations as critical
components of such a framework. Our questionnaires are publicly available to
facilitate the dissemination of knowledge in the field of XAI.
