Oprea Simona-Vasilica, Bâra Adela
Bucharest University of Economic Studies, Department of Economic Informatics and Cybernetics, No. 6 Piaţa Romană, Bucharest, 010374, Romania.
Heliyon. 2024 Nov 8;10(22):e40268. doi: 10.1016/j.heliyon.2024.e40268. eCollection 2024 Nov 30.
Three AI developments, classified as forms of human enhancements, center around progress at the intersection of AI, nanotechnology and biotechnology. Our research advances the understanding of AI and human enhancement by data-driven analytics and offers practical tools for future research and societal applications. It is based on a survey that was launched by PRC in February 2021 to more than 5,000 respondents from the U.S. It consists of about 100 questions that are grouped using a prefix code by the 3 above-mentioned human enhancement, science role, concerns and excitements, perceived algorithm fairness and demographics. To investigate this survey and extract insights regarding the general attitude, a data analytics framework is proposed that consists of clustering using DBSCAN and K-means, ANOVA for clusters, PCA, t-SNE and UMAP for graphical visualization, prediction and advanced customers' profiles analyses. Both clustering methods indicate distinct profiles for AI customers. Most of them are moderate, but two smaller groups define the tech-ethics advocates and tech-forward visionaries. For prediction, in the multi-class classification task, the ROC-AUC score is 0.852 and average F1 Score is 0.987. Following the results, both technology creators and legislators must work collaboratively to ensure that technological advancements are ethically grounded, widely accepted and aligned with societal values.
三种被归类为人类增强形式的人工智能发展，围绕着人工智能、纳米技术和生物技术交叉领域的进展。我们的研究通过数据驱动的分析推进了对人工智能和人类增强的理解，并为未来研究和社会应用提供了实用工具。它基于中国在2021年2月对5000多名美国受访者发起的一项调查。该调查由大约100个问题组成，这些问题按照上述人类增强、科学作用、关注点和兴奋点、感知算法公平性以及人口统计学的前缀代码进行分组。为了研究这项调查并提取有关总体态度的见解，提出了一个数据分析框架，该框架包括使用DBSCAN和K均值进行聚类、对聚类进行方差分析、使用主成分分析（PCA）、t-SNE和UMAP进行图形可视化、预测以及高级客户画像分析。两种聚类方法都表明了人工智能客户的不同特征。他们中的大多数人态度适中，但有两个较小的群体分别定义了技术伦理倡导者和技术前沿空想家。在多类分类任务中进行预测时，ROC-AUC分数为0.852，平均F1分数为0.987。根据这些结果，技术创造者和立法者都必须共同努力，以确保技术进步有道德依据、被广泛接受并与社会价值观保持一致。
