Towards the Explanation Consistency of Citizen Groups in Happiness Prediction via Factor Decorrelation

The happiness level of citizen groups has been widely analyzed using machine learning methods with explanation, aiming to support informed decision-making in our society. However, caused of complex correlations between happiness factors, there is inconsistency in case-by-case explanations provided by different models. In response, we propose a novel and trustworthy explanation solution for happiness prediction that can identify a broadly acceptable key factor set to improve explanation consistency across various models. First, the factor decorrelation is employed to ensure competitively high prediction accuracy. Second, we utilized a happiness prediction model pool that includes trained models with competitive accuracy, contributing to consistent explanations. The factor contribution is then computed using a post-hoc method based on the Shapley value with theoretical properties. The final key factor set is determined by the intersection of sets across different models. Experimental results using the Chinese General Social Survey (CGSS) and the European Social Survey (ESS) datasets validate the 2-fold increase in explanation consistency. Represented by specific citizen groups built on age, comprised of young group ($\leq$40) and elder group ($>$40), and health, comprised of bad health (1-3) and good health (4-5), we demonstrate how these demographics exhibit different contributions in terms of factors. Additionally, we leverage four objective metrics to further evaluate the explanation quality and a human perspective metric for evaluating explanation consistency by comparing our results against explanatory and descriptive studies to provide qualitative reliability measures.