Federated Learning (FL) has been foundational in improving the performance of a wide
range of applications since it was first introduced by Google. Some of the most prominent …

Artificial intelligence (AI) is one of the emerging technologies. In recent decades, artificial
intelligence (AI) has gained widespread acceptance in a variety of fields, including virtual …

In recent years, the problem of addressing fairness in machine learning (ML) and automatic
decision making has attracted a lot of attention in the scientific communities dealing with …

The original" Seven Motifs" set forth a roadmap of essential methods for the field of scientific
computing, where a motif is an algorithmic method that captures a pattern of computation …

Given the tremendous potential and influence of artificial intelligence (AI) and algorithmic
decision‐making (DM), these systems have found wide‐ranging applications across diverse …

Applying computational statistics or machine learning methods to data is a key component of
many scientific studies, in any field, but alone might not be sufficient to generate robust and …

Diagnosing and mitigating changes in model fairness under distribution shift is an important
component of the safe deployment of machine learning in healthcare settings. Importantly …

Despite years of research, the mechanisms governing the onset, relapse, symptomatology,
and treatment of schizophrenia (SZ) remain elusive. The lack of appropriate analytic tools to …

The explainable human–computer interaction (HCI) is about designing approaches capable
of using cognitive characteristics like humans. One such characteristic is human vision and …

The increasing reliance on ML models in high-stakes tasks has raised a major concern
about fairness violations. Although there has been a surge of work that improves algorithmic …