When machine learning models deliver problematic results, it can often happen in ways that humans can't make sense of, and this becomes dangerous when there are no limitations of the model, ...

Automated machine learning has long promised to hand the power of deep learning to scientists who never trained as programmers, yet most of these tools deliver a finished model with little explanation ...

Researchers in China have built an explainable AI framework that predicts student readiness for Industry 4.0 careers with ...

AI systems have tremendous potential, but the average user has little visibility and knowledge on how the machines make their decisions. AI explainability can build trust and further push the ...

Are deep learning models truly untrustworthy? Or are we simply governed by the naive intuition that 'what cannot be explained ...

Using a real-world, nationwide electronic health record–derived deidentified database of 38,048 patients with advanced NSCLC, we trained binary prediction algorithms to predict likelihood of 12-month ...

In the realm of Intensive Outpatient Programs (IOP), machine learning is reshaping how facilities such as those in Scottsdale operate. By integrating advanced ...

Lung cancer (LC) is a leading cause of cancer-related mortality in the United States. Accurate prediction of LC mortality rates is crucial for guiding targeted interventions and addressing health ...

Scientists have developed and tested a deep-learning model that could support clinicians by providing accurate results and clear, explainable insights—including a model-estimated probability score for ...

This course explores the field of Explainable AI (XAI), focusing on techniques to make complex machine learning models more transparent and interpretable. Students will learn about the need for XAI, ...