Applying risk management to deep learning models The black box problem presents a second challenge to business stakeholders with respect to risk management.

ZAC Cognitive Explainable-AI (CXAI) algorithms have many major advantages over other AI/ ML algorithms in the industry and academia, including the industry’s state-of-the-art, such as Deep ...

By using explainable AI, the study identifies not only which skills define each cluster but also the relative importance of these skills in classification decisions. This level of interpretability ...

Scientists and developers are deploying deep learning algorithms in sensitive fields such as medical imaging analysis and self-driving cars. There is concern, however, about how these AI operate.

Learn about artificial intelligence, its transparency challenges, and how explainable AI increases accountability and interpretability.

Explainable AI: A guide for making black box machine learning models explainable In the future, AI will explain itself, and interpretability could boost machine intelligence research.

UL’s Dr Alison O’Connor explains why we need explainable AI and how it can benefit the healthcare system.

The growing trend of AI means that it’s business-critical to understand how AI-enabled systems arrive at specific outputs.