For instance, machine-learning developers might focus on having features that are compatible with the model and predictive, meaning they are expected to improve the model's performance.

Finding another model that mimics a machine-learning model's predictions but uses transparent reasoning patterns is one way to understand it. However, because modern neural network models are so ...

All in all, not all advanced machine learning models are black box, and for most applications, a degree of explainability is sufficient to meet legal and regulatory requirements.

Given the increasing complexity of machine learning models, the need for model explainability has been growing lately. Some governments have also introduced stricter regulations that mandate a right ...

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

Someday machine learning models may be more ‘glass box‘ than black box. Until then, explainability tools and techniques can help us understand how a black box model makes its decisions.

The "explainability" of machine learning (ML) systems is often framed as a technical challenge for the communities who design artificial intelligence systems.

Does your model work? Can it explain itself? Heather Gorr talks about explainability and machine learning. You can send press releases for new products for possible coverage on the website. I am ...

“Machine learning is a method of analyzing data using an analytical model that is built automatically, or ‘learned’, from training data,” said Rick Negrin, who is the VP of Product ...