Interactive learning of explainable, situation-adapted decision models (C02)

• Subject Area: Methods in Artificial Intelligence and Machine Learning ; Management and Marketing
• Term: since 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 438445824

C02 aims to optimize ML-based decision support systems and human-AI interaction in order to improve decision-making. We focus on explainable and transparent models that take the decision context into account. We are focusing on prescriptive decision-making with partial feedback, where we not only consider the long-term impact of decisions but also improve the integration of Decision Makers (DMs) by accounting for their mental models and involving them in cases for exploration.

• DFG Programme: CRC/Transregios
• Subproject of: TRR 318: Constructing explainability
• Applicant Institution: Universität Paderborn
• Project Heads: Professor Dr. Eyke Hüllermeier; Professorin Dr. Kirsten Thommes