Why AI black boxes matter. In many cases, there is good reason to be wary of black box machine-learning algorithms and models. Suppose a machine-learning model has made a diagnosis about your health.

Any of the three components of a machine-learning system can be hidden, or in a black box. As is often the case, the algorithm is publicly known, which makes putting it in a black box less effective.

The term "black box" came from Great Britain’s Royal Air Force during WWII, Dr. Michael Capps told Fox News Digital. But when it relates to AI, the term is used to describe a decision-making ...

For enterprise adoption to achieve its full potential, particularly in mission and safety-critical applications, several ...

Learning from the lessons of the past interview Anuj Kapur, CEO of DevOps darling CloudBees, reckons that AI could retest the ...

In AI, especially with deep learning models, neural networks, or machine learning algorithms, "black box" systems have become a concern because they lack interpretability.

Black box machine learning does lead to some major challenges, including difficulties in achieving AI transparency and model interpretability. ...

A team led by AAIB director general Yugandhar, along with experts from AAIB and the US NTSB, has begun extracting data from ...

Despite their technical brilliance, existing machine learning (ML) and LLM-based systems struggle in real-world healthcare ...

This is a major research topic within theoretical machine learning, but my talk will focus on its engineering implications tailored to autonomous systems. The central question is the following: given ...