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 ...

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, ...

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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 ...

As artificial intelligence usage continues to increase, there’s a problem lurking in the background growing larger by the day: It’s the ability of AI to explain itself so it’s clear what led to an ...

For nearly half a century, economists and operations researchers have relied on a deceptively simple question to judge how well organizations use their resources: given the inputs a firm consumes, how ...

Systematic mapping of gender disparities in oncology publications of north African countries: The GEORGiNA study. This is an ASCO Meeting Abstract from the ASCO Breakthrough: A Global Summit for ...

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 ...

While the idea of a black box is intriguing, it can miss the point. Placing information into a black box that spits out a solution is valuable but understanding what’s going on in that black box is ...

While machine learning and deep learning models often produce good classifications and predictions, they are almost never perfect. Models almost always have some percentage of false positive and false ...