Researcher - advanced machine learning and decision making transparency

H3Researcher - Advanced Machine Learning and Decision Making Transparency /h3pProject: Interpretability and explainability methods for tree-based ensemble models: theoretical developments and economic applications. /ppThis project explores the intersection of advanced Machine Learning and decision-making transparency. Although tree-based ensemble models (such as Random Forest and XGBoost) provide enhanced predictive performance, their "black-box" nature often hinders their adoption in highly regulated sectors. This research aims to develop and refine Explainable AI(XAI) methods to make these models interpretable without compromising accuracy. The theoretical framework investigates novel feature attribution metrics, while the applied component validates these tools using economic datasets, addressing complex issues such as credit scoring, financial market forecasting, and public policy analysis. /ppDuration: 12 MonthsbrLead Researcher: Prof. Massimo AriabrRequirements: PhD in Economics or StatisticsbrStart Deadline 01 July 2026 /p #J-18808-Ljbffr