Using artificial intelligence, researchers show how γ-secretase recognizes substrates - an important advance for fundamental ...

As such, explainable AI is necessary to help companies pick up on the "subtle and deep biases that can creep into data that is fed into these complex algorithms.

Explainable AI (XAI) is an emerging field in machine learning that aims to address how black box decisions of AI systems are made. This area inspects and tries to understand the steps and models ...

Researchers from DZNE, Ludwig-Maximilians-Universität München (LMU), and Technical University of Munich (TUM) have found that ...

Explainable AI also allows us to understand whether bias comes from the data that the AI model is trained with or how different labels are weighted by the model. A matter of trust . For many people, ...

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.

As tech writer Scott Clark noted on CMSWire recently, explainable AI provides necessary insight into the decision-making process to allow users to understand why it is behaving the way it is.