Explaining evolving outliers for uncovering key aspects of the green comparative advantage

This paper deals with the outlier explanation problem, where the goal is to find a justification explaining what makes a known outlier different from the remaining data samples. To perform this task, we propose transformation-based explanations, which are a new kind of explanations we have recently defined (Angiulli et al., 2023) that, compared to other explanation types found in the literature, provide richer insights into the explanation task at hand, allowing decision-makers to take more informed actions. However, in several interesting application cases, decision-makers are required to analyse evolving scenarios, so that the ${\text{M}}^2\text{OE}$ method (Angiulli et al., 2024) we had proposed cannot be directly exploited.

Therefore, in this paper, an extension of the ${\text{M}}^2\text{OE}$ method, called ${\text{M}}^2{\text{OE}}_e$, is presented that constructs transformation-based explanations in evolving data scenarios. Moreover, we consider the application of the ${\text{M}}^2{\text{OE}}_e$ method to real-world environmental data. In particular, to test the effectiveness of ${\text{M}}^2{\text{OE}}_e$ in providing useful and actionable results within evolving data contexts, we gathered data concerning seven indicators related to the comparative advantage of low-carbon technologies in the 2016–2017, 2018–2019 and 2020–2021 time periods, and reshaped them in the Evolution of Green Comparative Advantage ($\text{evGreenCA}$) data collection. Then, we applied ${\text{M}}^2{\text{OE}}_e$ onto this data collection to quantitatively and qualitatively assess the interestingness of explanations it provides to decision makers in the considered application scenario.