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.

该研究通过调查和与专家的讨论，旨在找到适用于各个用例的通用解释框架的要素，并将这些洞察力纳入到一个利用GP算法的软件工具中。由于对可解释性的普遍偏好，可 sacrificed 实现准确性为代价。此外，我们强调了特征重要性和反事实解释作为该框架的关键组成部分的重要性。在XAI领域中推广知识，我们的问卷调查结果是公开可用的。

探索解释框架的共同点：多领域调查分析