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, ...

A systematic review of 237 studies from 2014 to 2024 maps how explainable AI techniques such as SHAP and LIME are making ...

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

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 ...

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 ...

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 ...

A Skoltech-developed approach combines machine learning, explainable AI, and multi-objective optimization to improve drilling ...

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 ...

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

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 ...