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Exploring excitement counterbalanced by concerns towards AI technology using a descriptive-prescriptive data processing method

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  • Simona-Vasilica Oprea

    (Bucharest University of Economic Studies)

  • Adela Bâra

    (Bucharest University of Economic Studies)

Abstract

Given the current pace of technological advancement and its pervasive impact on society, understanding public sentiment is essential. The usage of AI in social media, facial recognition, and driverless cars has been scrutinized using the data collected by a complex survey. To extract insights from data, a descriptive-prescriptive hybrid data processing method is proposed. It includes graphical visualization, cross-tabulation to identify patterns and correlations, clustering using K-means, principal component analysis (PCA) enabling 3D cluster representation, analysis of variance (ANOVA) of clusters, and forecasting potential leveraged by Random Forest to predict clusters. Three well-separated clusters with a silhouette score of 0.828 provide the profile of the respondents. The affiliation of a respondent to a particular cluster is assessed by an F1 score of 0.99 for the test set and 0.98 for the out-of-sample set. With over 5000 respondents answering over 120 questions, the dataset reveals interesting opinions and concerns regarding AI technologies that have to be handled to facilitate AI acceptance and adoption. Its findings have the potential to shape meaningful dialog and policy, ensuring that the evolution of technology aligns with the values and needs of the people.

Suggested Citation

  • Simona-Vasilica Oprea & Adela Bâra, 2024. "Exploring excitement counterbalanced by concerns towards AI technology using a descriptive-prescriptive data processing method," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-24, December.
    • Handle: RePEc:pal:palcom:v:11:y:2024:i:1:d:10.1057_s41599-024-02926-5
    DOI: 10.1057/s41599-024-02926-5
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    References listed on IDEAS

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    1. Jiang, Zhangsheng & Xu, Chenghao, 2023. "Policy incentives, government subsidies, and technological innovation in new energy vehicle enterprises: Evidence from China," Energy Policy, Elsevier, vol. 177(C).
    2. Jing Wang & Zeyu Xing & Rui Zhang, 2023. "AI technology application and employee responsibility," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-17, December.
    3. Mahfuzur Rahman & Teoh Hui Ming & Tarannum Azim Baigh & Moniruzzaman Sarker, 2021. "Adoption of artificial intelligence in banking services: an empirical analysis," International Journal of Emerging Markets, Emerald Group Publishing Limited, vol. 18(10), pages 4270-4300, December.
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