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 ...

Artificial intelligence (AI) may have more to offer science than accurate predictions. The patterns it learns could point ...

AI explainability and AI interpretability are notions often used interchangeably, despite immense differences in intention and practical application. This can be fine for high-level conversations ...

Two of the biggest questions associated with AI are “why does AI do what it does”? and “how does it do it?” Depending on the context in which the AI algorithm is used, those questions can be mere ...

SALT LAKE CITY, UTAH – Researchers at the University of Utah's Department of Psychiatry and Huntsman Mental Health Institute today published a paper introducing RiskPath, an open source software ...

American insurers are being urged not to drag their feet on ensuring their use of AI is “explainable,” as regulators and consumers alike begin to demand it. “It’s not like this is a future issue. The ...

Richard Jones, VP of product at ExpenseIn, argues that black box AI cannot police expense fraud and finance teams need ...

When AI falters, it’s easy to blame the model. People assume the algorithm got it wrong or that the technology can’t be trusted. But here’s what I've learned after years of building AI systems at ...

An area of great hope and promise for applied artificial intelligence (AI) deep learning is at the intersection of neuroscience and oncology, both challenging fields known for their inherent ...

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 ...