Quantifying Multi-Level Effects of AI Adoption through an End-to-End Machine Learning Pipeline

Na Tang1,2 and Yang Yang1

  1. Business School, Geely University of China,
    No. 123, Section 2, Chengjian Avenue, Chengdu, 610225, China
    tangna@guc.edu.cn
    yangyang@guc.edu.cn (corresponding author)
  2. Faculty of Economics and Business, Universiti Malaya
    Jalan Universiti, 50603 Kuala Lumpur, Malaysia
    s2134677@siswa.um.edu.my

Abstract

Artificial Intelligence is reshaping global socio-economic structures, yet existing studies often analyze its economic, social, and market impacts in isolation, lacking a unified framework that handles multi-dimensional interactions. This paper constructs an end-to-end machine learning pipeline — from raw heterogeneous data input, through three cascaded modules (micro: Random Forest + SHAP; meso: XGBoost; macro: K-means++ clustering), to actionable policy outputs — to quantify the complex socio-economic effects of AI adoption across countries and industries. Using a comprehensive global dataset (2020–2025), this research finds that: (1) Human-AI Collaboration Rate (HAICR) is the dominant mitigator of AI-driven job loss, outweighing AI Adoption Rate (AIAD), while high AIAD significantly exacerbates job displacement in automated sectors like manufacturing and retail; (2) Strict regulation is the strongest driver of consumer trust, but its efficacy is moderated by cultural contexts and tool transparency; (3) China and the U.S. lead AI-driven markets, whereas India and South Korea require infrastructure and policy upgrades. By coupling machine learning methods in a tripartite analytical pipeline, this study moves beyond isolated econometric models and provides quantifiable, actionable insights for balancing AI innovation with social welfare.

Key words

Artificial Intelligence Adoption, Machine Learning, Multi-Dimensional Socio-Economic Impact, Human-AI Collaboration Rate, AI Regulation Status

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS251101037T

Publication information

Volume 23, Issue 4 (September 2026)
Year of Publication: 2026
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Tang, N., Yang, Y.: Quantifying Multi-Level Effects of AI Adoption through an End-to-End Machine Learning Pipeline. Computer Science and Information Systems, 23(4) (2026). https://doi.org/10.2298/CSIS251101037T