The emergence of generative AI has brought forth significant concerns regarding AI misapplications on a global scale. This paper presents an explainable AI framework, leveraging SHAP (SHapley Additive exPlanations), to illuminate potential societal impacts and risks associated with these technologies. We emphasize the critical importance of understanding the tools and methodologies that researchers worldwide have implemented and distributed. Furthermore, we propose a paradigm shift in researchResearch practices, advocating for responsible and ethical AI applications that benefit society at large. Despite the urgency of addressing AI misapplications, prominent scientific publications, including Nature, Science, and Cell, have not adequately addressed these concerns, suggesting a potential gap in editorial awareness regarding the broader implications of AI technologies. To bridge this gap in academic discourse, we recommend robustRobust statistical approaches for evaluating AI systemsSystems, including nonlinearNonlinear and nonparametric pairwise methods such as Spearman’s correlation with p-values, Kendalls tau with p-values, Goodman–Kruskal gamma with p-values, Somers’ D with p-values, and Hoeffding's D with p-values, while Mutual InformationInformation (MI) analysisAnalysis examines complex multivariate interactions. This comprehensive approach aims to establish more rigorous standards for AI researchResearch and promote responsible innovation in the fieldField.