Explainable Artificial Intelligence (XAI) seeks to render the operation and decisions of complex machine learning systems transparent and interpretable to users, regulators and other stakeholders. As ...

In past roles, I’ve spent countless hours trying to understand why state-of-the-art models produced subpar outputs. The underlying issue here is that machine learning models don’t “think” like humans ...

Across the UK, financial institutions are using machine learning models to make decisions that affect millions of people. These decisions include credit approvals, fraud alerts, investment ...

They also add pressure to already crowded emergency departments.” In a new five-year, nearly $4 million project, funded by ...

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 ...

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 ...

A body of trustworthy AI research addresses interpretability, privacy, and robustness through rule-based modeling, federated learning, and adversarial defense. The studies explore how AI systems can ...

A one-size-fits-all approach likely isn't the best strategy when designing artificial intelligence systems that assist users in disease diagnosis.

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, ...

Researchers at Indiana University School of Medicine have developed and tested a six-point scoring system that can help ...