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. Explanation methods ...

With mountains of patient data on hand, and more insights emanating from them through the power of machine learning, how can health care get the most out of bleeding-edge technologies? For Fred ...

In a global report issued by S&P, 95% of enterprises across various industries said that Artificial Intelligence (AI) adoption is an important part of their digital transformation journey. We’re ...

AI explainability remains an important preoccupation - enough so to earn the shiny acronym of XAI. There are notable developments in AI explainability and interpretability to assess. How much progress ...

Enterprise-grade explainability solutions provide fundamental transparency into how machine learning models make decisions, as well as broader assessments of model quality and fairness. Is yours up to ...

The "explainability" of machine learning (ML) systems is often framed as a technical challenge for the communities who design artificial intelligence systems. However, in a Policy Forum, Diane Coyle ...

Forbes contributors publish independent expert analyses and insights. People distrust artificial intelligence and in some ways this makes sense. With the desire to create the best performing AI models ...

According to a new study, machine learning can reliably identify patients at high risk of early dysphagia following acute ...