Explainable AI (XAI) is a set of techniques, design principles, and processes that help developers/organizations add a layer of transparency to AI algorithms.

Here we explore the essence of explainability in AI and analyzing how applies to decision support systems in healthcare, finance, and other different industries

A preambular article describing the fundamental principles & intuition behind the “LIME” concept in Artificial Intelligence & Machine learning.

Discover how the latest developments in AI interpretability are making AI more trustworthy. In a world relying more on AI, it's crucial to know we can trust it.

AI solutions in traditional banking and lending still largely remain "black boxes", where no one really seems to know how AI models are generating their results

An interview with Yotam Azriel, CTO at TensorLeap - What's AI episode 13

A first look at Genius™ - redefining the future of AI through its innovative approach rooted in Active Inference, First Principles AI, and Shared Intelligence.

Looking to implement cybersecurity automation in your business? Here are the most common challenges to prepare for.

How verifiable machine intelligence is transforming machine learning

Explore the journey from HAL-9000's fictional AI to the present-day role of AI in political campaigns.

AI's "black box" dilemma grows as conversations with it become everyday norms, veiling true understanding.

Explore a cutting-edge pipeline for detecting and explaining anomalous reviews in online platforms like Amazon.

Explore findings from a study on an anomaly detection pipeline for Amazon reviews using MPNet embeddings.

Explore findings from a study on an anomaly detection pipeline for Amazon reviews using MPNet embeddings.

Explore a comprehensive evaluation of anomaly detection methods using Amazon review data.

Explore recent advancements in anomaly detection for texts, focusing on techniques like NLP and machine learning classifiers.

Discover a robust pipeline for detecting and explaining anomalous reviews in online platforms like Amazon.

Explainable AI is hot. The promise of transparency is noble, but the trade-offs could stifle AI’s real potential. Is chasing “why” worth derailing progress?