Nghiên cứu ứng dụng mô hình học máy khả diễn XAI trong phân tích rủi ro đầu tư ESG: Thông tin chi tiết từ các doanh nghiệp S&P 500 - Hoang, Anh; Truong Chuc, Thanh; Le Thi Tuyet, Mai (JOURNAL OF ASIAN BUSINESS AND ECONOMIC STUDIES, 2025)
Environmental, Social, and Governance (ESG) factors have become crucial in assessing corporate performance and risks. This study examines the application of explainable artificial intelligence (XAI) to enhance interpretability and improve trustworthiness in ESG risk analysis. By experimenting with ESG data from S&P 500 companies, this study shows that the LightGBM model achieves the highest accuracy, outperforming XGBoost and Random Forest. SHAP value analysis indicates that ESG risks are primarily driven by three key factors: environmental risk, social risk, and governance risk. Additionally, the overall ESG risk level of the company also plays an important role. The findings highlight the potential of XAI models to enhance ESG reporting and compliance while providing valuable insights for investors and policymakers. Furthermore, this study demonstrates the viability of integrating explainable ML/AI approaches into organizational risk management, thereby fostering transparency, accountability, and trust in ESG assessments.