A growing number of artificial intelligence (AI)-based clinical decision support systems are
showing promising performance in preclinical, in silico, evaluation, but few have yet …

Background The increased adoption of the internet, social media, wearable devices, e-
health services, and other technology-driven services in medicine and healthcare has led to …

Deployed AI systems often do not work. They can be constructed haphazardly, deployed
indiscriminately, and promoted deceptively. However, despite this reality, scholars, the …

As individuals and communities interact in and with an environment that is increasingly
virtual, they are often vulnerable to the commodification of their digital footprint. Concepts …

In this paper, we review algorithmic bias in education, discussing the causes of that bias and
reviewing the empirical literature on the specific ways that algorithmic bias is known to have …

When Machine Learning technologies are used in contexts that affect citizens, companies as
well as researchers need to be confident that there will not be any unexpected social …

Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …

Large-scale language technologies are increasingly used in various forms of
communication with humans across different contexts. One particular use case for these …

Until now, much of the work on machine learning and health has focused on processes
inside the hospital or clinic. However, this represents only a narrow set of tasks and …

Graph mining algorithms have been playing a significant role in myriad fields over the years.
However, despite their promising performance on various graph analytical tasks, most of …