Ecological Approach to Increase Agent Transparency in Semi-Automated Air Traffic Control

Future ATM systems will rely on automation to make operations more efficient. Creating insight into the inner-workings of automation, also known as agent transparency, is expected to play an important role for effective human-machine collaboration. This research proposes an ecological approach to increase agent transparency in automated...

Predicting Intensive Care Unit Readmission: Performance and Explainability of Machine Learning Algorithms

Intensive Care Unit (ICU) readmission is a serious adverse event associated with high mortality rates and costs. Prediction of ICU readmission could support physicians in their decision to discharge patients from the ICU to lower care wards. Due to increasing ICU data availability, Artificial Intelligence (AI) models in the form of machine...

Human-centered XAI: Developing design patterns for explanations of clinical decision support systems

Much of the research on eXplainable Artificial Intelligence (XAI) has centered on providing transparency of machine learning models. More recently, the focus on human-centered approaches to XAI has increased. Yet, there is a lack of practical methods and examples on the integration of human factors into the development processes of AI...