Transparency and explainability are only way organizations can trust autonomous AI.

Explainability tools are commonly used in AI development to provide visibility into how models interpret data. In healthcare machine learning systems, explainability techniques may highlight factors ...

Explainability can support adolescents’ development in several ways. It can foster digital awareness by helping users recognize the role of algorithms in curating online content. This awareness ...

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 ...

AI has become a key part of our day-to-day lives and business operations. A report from Microsoft and EY that analysed the outlook for AI in 2019 and beyond, stated that “65% of organisations in ...

The global XAI market offers opportunities in delivering cloud-based and on-premise solutions for sectors like BFSI, healthcare, and retail. Key trends include growing demand for AI transparency and ...

While machine learning and deep learning models often produce good classifications and predictions, they are almost never perfect. Models almost always have some percentage of false positive and false ...

Explainable AI helps companies identify the factors and criteria algorithms use to reach decisions. (Photo by Jens Büttner/picture alliance via Getty Images) Artificial intelligence is biased. Human ...