One of the most important aspects of data science is building trust. This is especially true when you're working with machine learning and AI technologies, which are new and unfamiliar to many people.

Johanna Pingel is product marketing manager, MathWorks. AI is transforming engineering in nearly every industry and application area. With that, comes requirements for highly accurate AI models.

The key to enterprise-wide AI adoption is trust. Without transparency and explainability, organizations will find it difficult to implement success-driven AI initiatives. Interpretability doesn’t just ...

A visual representation of XAI. A clear white box model containing a digitized brain, with the letters X, A & I etched on the top of the box. According to the 2022 IBM Institute for Business Value ...

Artificial intelligence is seeing a massive amount of interest in healthcare, with scores of hospitals and health systems already have deployed the technology – more often than not on the ...

Does your model work? Can it explain itself? Heather Gorr talks about explainability and machine learning. You can send press releases for new products for possible coverage on the website. I am also ...

AI promises to enhance routing, reduce fraud, and improve straight‑through processing (STP), but it cannot function safely when the underlying payment architecture is opaque. Legacy payment ...

Deep Learning-Based Dynamic Risk Prediction of Venous Thromboembolism for Patients With Ovarian Cancer in Real-World Settings From Electronic Health Records Data collected in the multicentric PRAIS ...