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 ...

One way to gain explainability in AI systems is to use machine learning algorithms that are inherently explainable. For example, simpler forms of machine learning such as decision trees ...

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 ...

Because of this, more research is being focused on the explainability of models. Another challenge with machine learning is the need to form an experienced team. “To build this team in-house ...

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

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 ...

In a paper, Explainable AI in Practice Falls Short of Transparency Goals, the authors, Umang Bhatt et al, make the proposition that: PAI's research reveals a gap between how machine learning ...

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.