Kubit, the leading customer journey analytics platform, today announced the launch of Ask Kubit, a conversational AI ...

Explainable AI (XAI) bridges that gap. It offers human-readable insights into how AI models make decisions, allowing business ...

Users start with simple interactions and gradually discover more sophisticated capabilities as their comfort level increases.

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.

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.

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 ...

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.