We analyze how financial stability concerns discussed during Federal Open Market Committee (FOMC) meetings influence the Federal Reserve's monetary policy imple- mentation and communication. Utilizing large language models (LLMs) to analyze FOMC minutes from 1993 to 2022, we measure both...

Interest in predicting sports match outcomes has grown significantly, driven by advancements in machine learning techniques and widespread adoption. However, the utilization of these predictive models in enhancing tactical team performance remains relatively limited. We propose a methodology...

We analyze how financial stability concerns discussed during Federal Open Market Committee (FOMC) meetings influence the Federal Reserve's monetary policy imple- mentation and communication. Utilizing large language models (LLMs) to analyze FOMC minutes from 1993 to 2022, we measure both...

The use of explainable AI (XAI) methods to render the prediction logic of black-box AI interpretable to humans is becoming more popular and more widely used in practice, among other things due to regulatory requirements such as the EU AI Act. Previous research on human-XAI interaction has shown...

Machine learning is permeating every conceivable domain to promote data-driven decision support. The focus is often on advanced black-box models due to their assumed performance advantages, whereas interpretable models are often associated with inferior predictive qualities. More recently,...