In healthcare, the development and deployment of insufficiently fair systems of artificial
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …

The adoption of machine learning (ML) and, more specifically, deep learning (DL)
applications into all major areas of our lives is underway. The development of trustworthy AI …

Real-world machine learning systems often encounter model performance degradation due
to distributional shifts in the underlying data generating process (DGP). Existing approaches …

In many machine learning for healthcare tasks, standard datasets are constructed by
amassing data across many, often fundamentally dissimilar, sources. But when does adding …

Machine learning models, while progressively advanced, rely heavily on the IID assumption,
which is often unfulfilled in practice due to inevitable distribution shifts. This renders them …

Monitoring and maintaining machine learning models are among the most critical
challenges in translating recent advances in the field into real-world applications. However …

In this paper, we investigate the practical relevance of explainable artificial intelligence (XAI)
with a special focus on the producing industries and relate them to the current state of …

This study aimed to compare and evaluate the prediction accuracy and risk of bias (ROB) of
post-traumatic stress disorder (PTSD) predictive models. We conducted a systematic review …

Monitoring machine learning systems and efficiently recovering their reliability after
performance degradation are two of the most critical issues in real-world applications …

As input data distributions evolve, the predictive performance of machine learning models
tends to deteriorate. In practice, new input data tend to come without target labels. Then …