Explainability is now a requirement for institutions deploying AI in financial crime compliance. It supports better ...

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 ...

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.

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

A Future with Explainable AI. Explainable AI is the future of business decision-making. Explainable decision making plays a role in every aspect of AI solutions from training, QA, deployment, ...

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.

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. “And that’s ...

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. However, ...

Explainable AI addresses this limitation by providing insight into the model’s decision-making process,” the Virginia Tech team notes. The study authors actually created and tested an MPEA ...