The study examined how often participants changed their mind after seeing the AI’s output. When the AI agreed with their initial choice, participants rarely engaged deeply with the explanation, often ...

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

US], August 14: Narwal, an award-winning niche technology solutions company, is excited to announce the appointment of Ravi ...

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

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

AI is rapidly reshaping the landscape of fraud prevention, creating new opportunities for defense as well as new avenues for deception.

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.

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 ...

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.