Organizations are realizing that in order for clinicians to adopt machine learning tools, they need to understand the suggestions. At Geneia, what we do is push for the explainability of insights.

Researchers have created a taxonomy and outlined steps that developers can take to design features in machine-learning models ... to work backward and focus on explainability after the fact.

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 ...

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 ...

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

With the desire to create the best performing AI models, many organizations have prioritized complexity over the concepts of explainability ... people who produce machine learning models such ...

But our question as two AI practitioners… Is explainability that important ... and trust the results and output created by machine learning algorithms. Many believe that XAI promotes model ...

Explanation methods that help users understand and trust machine-learning models often describe ... rather than trying to work backward and focus on explainability after the fact.