Scholarlink

<p>The systematic integration of artificial intelligence (AI) within business practices and corporate governance has made Responsible AI (RAI) an absolute subset of corporate social responsibility. This research formulates a framework that integrates Responsible AI principles with hypothesis-driven rule extraction to predict Environmental, Social, and Governance (ESG) scores. Employing the ensemble-based RuleFit algorithm, we construct decision rules that algorithmically associate non-linearly with firm-level attributes such as size, leverage, digitization intensity, and managerial ownership. These rules are then transformed into domain-specific hypotheses and subsequently subjected to statistical validation using nonparametric tests to ensure robust results under non-normal outcome distributions. Performance predictive models are augmented to preserve equity by simultaneously assessing subgroup fairness through the distribution of residuals and biased proportions of firm characteristics. In summary, the proposed methodology illustrates how rule-based learning can yield interpretable, statistically sound, and ethically aligned forecasts of ESG outcomes, thereby facilitating reliable decision-making in sustainable finance and corporate governance.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A hypothesis-driven responsible AI framework for interpretable ESG forecasting with RuleFit

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月28日

查看全文
  • Tufail Muhammad,
  • Rubab Hafeez,
  • Waqas Bin Khidmat,
  • Mehtab Afzal,
  • Fazeel Abid,
  • Harun Elkiran,
  • Ahmet Gurhanli,
  • Jawad Rasheed

摘要

The systematic integration of artificial intelligence (AI) within business practices and corporate governance has made Responsible AI (RAI) an absolute subset of corporate social responsibility. This research formulates a framework that integrates Responsible AI principles with hypothesis-driven rule extraction to predict Environmental, Social, and Governance (ESG) scores. Employing the ensemble-based RuleFit algorithm, we construct decision rules that algorithmically associate non-linearly with firm-level attributes such as size, leverage, digitization intensity, and managerial ownership. These rules are then transformed into domain-specific hypotheses and subsequently subjected to statistical validation using nonparametric tests to ensure robust results under non-normal outcome distributions. Performance predictive models are augmented to preserve equity by simultaneously assessing subgroup fairness through the distribution of residuals and biased proportions of firm characteristics. In summary, the proposed methodology illustrates how rule-based learning can yield interpretable, statistically sound, and ethically aligned forecasts of ESG outcomes, thereby facilitating reliable decision-making in sustainable finance and corporate governance.