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 ...

HEX tailors machine learning explanations to match human decision-making preferences, boosting trust and reliability in high-stakes scenarios. HEX: Human-in-the-loop explainability via deep ...

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 ...

For the highest chances of success in machine learning, test your model early with an MVP and invest the necessary time and money to diagnose and fix its weaknesses.

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

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.

“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 ...

Accessible Labs Ltd., which does business as cnvrg.io, today released a dashboard that can be used by developers of machine learning models to optimize their use of server resources.The Jerusalem- ...