This collection welcomes submissions on explainability techniques for deep learning neural networks, encompassing diverse neural architectures and ensuring broad applicability to different domains.

Amazon Web Services is adding an AI explainability reporting feature to its SageMaker machine learning model builder aimed at improving model accuracy.

And “deep reinforcement learning,” as implemented in autonomous robots, self-driving cars, and creation of images, voices, and videos, is far from being widely available.

The integration of deep learning in neuroimaging enhances diagnostic capabilities, offering new insights into neurological disorders and treatment responses.

Researchers introduced HEX, a human-in-the-loop deep reinforcement learning method that improves trust and explanation quality in machine learning models used in high-stakes decision-making.

Building explainability into the components of machine-learning models Researchers develop tools to help data scientists make the features used in machine-learning models more understandable for ...

How do we balance the potential benefits of deep learning with the need for explainability? Getty People distrust artificial intelligence and in some ways this makes sense.

Explainability helps business leaders understand why a company is doing what they’re doing with AI. More important is what practitioners of AI call “explainability.” ...

Wall Street firms are putting more resources towards complex forms of machine learning, known as deep learning, a recent survey by Refinitiv found.