This chapter examines problems related to governmental policies, emphasizing answers to two types of suggestions: human and artificial intelligence based. The study aims to determine whether there is a difference between human-driven and AI-driven studies. More precisely, how effectively do AI “think” concepts compared to humans, or perhaps even surpass them? Thus, there is a need to compare general patterns emerging from the two studies on the same topic. There are several ways to compare the patterns of responses generated by the two experiments, ranging from the patterns of ratings, the relations between the presence/absence of elements versus the response to the vignette (modeling), the interpretation of strong performing elements (viz., interpretation and AI-based summarization), and finally the division of the respondents into mind-sets by clustering and the nature of these clusters. The Index of Divergent Thought serves as a crucial tool in this study, enabling the evaluation of each approach’s performance. To link the newly created binary variables and response time to the presence or absence of elements, ordinary least squares regression is employed. Mind Genomics is a developing discipline now, gaining popularity in studying environmental issues due to its methodical approach to handling ideas’ responses.