Researchers have created a taxonomy and outlined steps that developers can take to design features in machine-learning models that are easier for decision-makers to understand.

Many believe that XAI promotes model transparency and trust, making people more comfortable with the risk of improper learning and incorrect predictions that can occur with machine learning models.

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

New deep learning models: Fewer neurons, more intelligence Date: October 13, 2020 Source: Institute of Science and Technology Austria Summary: An international research team has developed a new ...

Modern machine-learning models, such as neural networks, are frequently referred to as "black boxes" because they are so intricate that even the researchers who create them do not truly comprehend ...

Bloomberg is set to release further empirical metrics, at the end of this year, to bolster its liquidity models’ explainability.

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

However, achieving explainability in practice comes with challenges, particularly with the complexity of deep learning models, which often operate as black boxes.

Machine learning interview questions now focus on both theory and real-world applications.Understanding basics like overfitting, bias, and regres ...