The COVID-19 pandemic has been labeled as a black swan event that caused a ripple effect
on every aspect of human life. Despite the short time span of the pandemic—only four and …

Artificial intelligence (AI) has huge potential to improve the health and well-being of people,
but adoption in clinical practice is still limited. Lack of transparency is identified as one of the …

The public release of Large Language Models (LLMs) has sparked tremendous interest in
how humans will use Artificial Intelligence (AI) to accomplish a variety of tasks. In our study …

In recent years, machine learning has transitioned from a field of academic research interest
to a field capable of solving real-world business problems. However, the deployment of …

Background Interest in the application of machine learning (ML) to the design, conduct, and
analysis of clinical trials has grown, but the evidence base for such applications has not …

Despite the proliferation of explainable AI (XAI) methods, little is understood about end-
users' explainability needs and behaviors around XAI explanations. To address this gap and …

While a growing number of machine learning (ML) systems have been deployed in clinical
settings with the promise of improving patient care, many have struggled to gain adoption …

With the recent wave of progress in artificial intelligence (AI) has come a growing awareness
of the large-scale impacts of AI systems, and recognition that existing regulations and norms …

Recent advances in AI combine large language models (LLMs) with vision encoders that
bring forward unprecedented technical capabilities to leverage for a wide range of …

Research in Fairness, Accountability, Transparency, and Ethics (FATE) 1 has established
many sources and forms of algorithmic harm, in domains as diverse as health care, finance …