The review found that while AI is making inroads in specific use cases, such as computer vision for medical imaging, ...

As artificial intelligence reshapes industries, its transformative potential remains out of reach for many. While AI i ...

In its early years, AI was designed to be noticed. Recommendation engines claimed to predict your next purchase. Digit ...

AI in 2035 will see, hear and sense — offering real-time support tailored to you. With multimodal capabilities, assistants ...

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

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.

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 ...

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.

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.

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 ...

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 ...

Explainable AI begins with people. AI engineers can work with subject matter experts and learn about their domains, studying their work from an algorithm/process/detective perspective.