Z Advanced Computing, Inc. (ZAC), the pioneer Cognitive Explainable-AI (Artificial Intelligence) (Cognitive XAI or CXAI) ...

This is partly why explainable AI is not enough, says Anthony Habayeb, CEO of AI governance developer Monitaur. What’s really needed is understandable AI.

As an example, Paka explains how explainable AI can improve AI-based credit lending model used by banks. He says, "There are a number of inputs (like annual income, FICO score, etc.,) that are ...

With generative AI tools becoming commonplace in schools and universities, educators and policymakers are increasingly ...

In it, explainable AI is placed at the peak of inflated expectations. In other words, we have reached peak hype for explainable AI. To put that into perspective, a recap may be useful.

Explainable AI works to make these AI black-boxes more like AI glass-boxes. Although businesses understand the many benefits to AI and how it can provide a competitive advantage, they are still wary ...

IBM’s explainable AI toolkit, which launched in August 2019, draws on a number of different ways to explain outcomes, such as an algorithm that attempts to spotlight important missing ...

As AI and machine learning advance, requiring explainable AI and creating verifications of those explanations will become a check against malicious AI or AI that has simply gone off the rails.

An explainable AI yields two pieces of information: its decision and the explanation of that decision. This is an idea that has been proposed and explored before.

Why explainable AI matters According to a report released by KPMB and Forrester Research last year, only 21 percent of US executives have a high level of trust in their analytics.

“Explainable AI essentially helps you trust the algorithm at the end of the day, and it helps you explain to regulators what you’re doing is well-documented in the system, so no one can say ...