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This study presents an AI-enhanced framework to evaluate the social sustainability of working environments in European industrial systems. Focusing on four key dimensions (Gender Gap, Employment by Skill, Safety at Work, and Digital Skill Gap) the framework leverages machine learning techniques (clustering and decision trees) to classify and compare countries’ social sustainability profiles over time. Results highlight significant disparities across European nations and temporal improvements in some indicators, reflecting increased policy attention. The integrated human–AI approach supports transparent, data-driven decision-making for policymakers and collaborative networks, contributing to harmonized, socially sustainable industrial development across the EU.

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Leveraging AI Data Analytics Methodology for Social Sustainability of Working Environments: An Integrated Framework

  • 会议论文

  • 发表日期：

    2026年

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  • Diletta Tosetto,
  • Andrea Zangiacomi,
  • David F. Nettleton,
  • Giulia Perin,
  • Rosanna Fornasiero

摘要

This study presents an AI-enhanced framework to evaluate the social sustainability of working environments in European industrial systems. Focusing on four key dimensions (Gender Gap, Employment by Skill, Safety at Work, and Digital Skill Gap) the framework leverages machine learning techniques (clustering and decision trees) to classify and compare countries’ social sustainability profiles over time. Results highlight significant disparities across European nations and temporal improvements in some indicators, reflecting increased policy attention. The integrated human–AI approach supports transparent, data-driven decision-making for policymakers and collaborative networks, contributing to harmonized, socially sustainable industrial development across the EU.