Digital Signal Processing and Artificial Intelligence for Automatic Learning Yao-Liang Chung 5 2 2026 https://doi.org/10.6025/dspaial/2026/5/2/80-105 https://www.dline.info/dspai/fulltext/v5n2/dspaiv5n2_2.pdf 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.