@article{4756,
  author = {Yao-Liang Chung},
  title = {Explainable AI-Driven Workforce Intelligence Framework for Automation Risk Analysis and Occupational Transformation toward 2030},
  journal = {Digital Signal Processing and Artificial Intelligence for Automatic Learning},
  year = {2026},
  volume = {5},
  number = {2},
  doi = {https://doi.org/10.6025/dspaial/2026/5/2/80-105},
  url = {https://www.dline.info/dspai/fulltext/v5n2/dspaiv5n2_2.pdf},
  abstract = {The rapid integration of artificial intelligence (AI) into global labor markets is fundamentally reshaping
occupational structures and workforce dynamics. This study proposes an Explainable AI-Driven Workforce
Intelligence Framework for automation risk analysis and occupational transformation toward 2030.
Addressing critical gaps in interpretability within existing workforce-automation research, the framework
integrates predictive analytics, global and local explainability mechanisms, and attention-inspired feature
interdependency analysis to investigate how socioeconomic and technological variables collectively influence
occupational vulnerability. Using the AI Impact on Jobs 2030 dataset, a Gradient Boosting-based predictive
model classifies occupations into risk categories, while permutation-based feature attribution and Local
Interpretable Model-Agnostic Explanations (LIME) provide transparent interpretation of workforce-risk
determinants. Results reveal that automation feasibility indicators exert substantially stronger influence on
risk prediction than traditional socioeconomic variables, with Automation Probability 2030 emerging as the
dominant predictive anchor. Attention-inspired dependency analysis further demonstrates that high-risk
occupations exhibit intensified feature interdependencies, particularly between technological growth and
workforce experience, suggesting that rapid technological advancement may diminish the stabilizing influence
of professional tenure. The study contributes an integrated, interpretable analytical architecture that extends
explainability concepts from natural language processing to workforce intelligence, enabling transparent
occupational-risk assessment and evidence-driven labor-market forecasting. By illuminating the structural
mechanisms underlying workforce vulnerability, the framework supports policymakers, educational
institutions, and organizations in designing targeted reskilling initiatives and adaptive workforce strategies
for sustainable labor-market transformation in the era of AI-driven digital change.},
}