# Contextual Background Estimation for Explainable AI in Temperature Prediction
## 4 Discussion and Future Work  
This article is the next step towards improving the use of XAI, notably for power systems. It corroborates the need to provide new solutions regarding contextualized XAI methods for regression-based problems. This is the first article to discuss the significance of different backgrounds in the context of regression-based problems and shifting references. The proposed methods reveal a new chapter in the analysis of background significance for those problems. Even though this was the first attempt at finding the optimal method for the estimation of backgrounds, it was not limited to vector features but already tested and applied for two-dimensional time-series problems. It was proved that the methods are beneficial for estimating the stability of explanations and for finding clusters and distributions that can be used to better understand the behavior of the model. The natural next step would be to find a way to easily, and automatically, assign time-series sequences to specific clusters and types of backgrounds.  
The methods presented here are not limit-free. First of all, they are time-consuming. The calculation of Shapley values is time-consuming by itself, and it depends on the number of features. Additionally, much more time is required to calculate Shapley values within timeSHAP due to numerous time steps. Finally, each step, already time-consuming, needs to be repeated for each prepared background. This is mainly relevant in the Background Estimation Method and depends on the number of elements in the dataset. The next step would be to optimize the algorithm, for example, by detecting clusters of backgrounds beforehand or limiting the number of processed backgrounds based on contextual information.  
Undoubtedly, by limiting the dataset to a specific range of backgrounds, the interpretation of the behavior of a model as a whole can also be limited. The methods aim at exploring model behavior only in certain contextualized situations, not as a whole. So, by understanding why a model made a certain prediction in one context will not reveal much about its decisions in different contexts. These are context-specific methods. However, by performing a thorough analysis, with numerous base values, these methods could describe the behavior of a model better than the XAI method by itself, thus potentially being used as supports and estimators for the XAI method to provide even more information about the model by demonstrating the influence of the backgrounds.  
The solutions presented here have high significance for forecasts in power systems. They prove that, in this fragile field, the explanations provided by conventional XAI methods that use backgrounds cannot be fully trusted and there is a need for further verification. Kernel SHAP, used in this article, can be employed to determine the reasons for changes in predictions of temperature, which is the main indicator of electrical load. The utility index, which was presented here, can facilitate this process. It can be used whenever the user requires contextualized explanations by providing indications of the viability and utility of features for explanations in power systems.