Contact
Prof. Dr. Mathias Klier
+49 (0) 7 31 50-3 23 12
mathias.klier(at)uni-ulm.de
Artificial Intelligence (AI) offers great potential to support data-driven decision-making processes, particularly in the field of forecasting. However, in business practice, AI is used only hesitantly due to its “black box” nature. The lack of transparency in forecasts makes it difficult for users to understand, validate, and use them as a basis for decision-making.
A promising solution lies in the automated generation of explanations for AI-based forecasts without compromising forecast quality (keyword: “Explainable AI” (XAI)). The aim of the project is to prepare AI-based forecasts in a way that makes them understandable and interpretable for users with domain expertise.
The focus is not only on the technical implementation, but also on examining the effects of XAI on behavior and decision-making processes in controlling. As part of the project, suitable business use cases will first be identified, forming the basis for the development and real-world evaluation of an XAI system for controlling. This is intended to make a substantial contribution to the acceptance and practical usability of AI in the field of controlling.
Project partner: IPRI – International Performance Research Institute
Project duration: since September 2023
Transfer
The project has been funded by the Péter Horváth Foundation since September 2023.
It makes a significant contribution to the integration of artificial intelligence into business decision-making processes – particularly in the field of controlling. Through the targeted use of explainable AI (XAI), forecasts are prepared in a way that makes them understandable and verifiable for professionals – a key requirement for the trustworthy and responsible use of AI in companies. By developing and testing explainable AI-based forecasts in real-world scenarios, the project lays an important foundation for making data-driven planning and control processes fit for the future.
The project results provide a robust basis for the use of AI-based forecasts under real conditions and also offer guidance for further application areas. In addition, the project generates valuable insights into how AI is perceived by professionals in business practice and what factors influence its acceptance and use.