Скачать или смотреть Opening the Black Box

The explainer explains the critical concept of the AI black box paradox, where the most powerful AI models are often the least understandable, posing significant challenges for trust, fairness, and safety in high-stakes applications like medicine or justice. To address this, the explainer differentiates between interpretability (predicting what a model does) and explanability (understanding why it does it), presenting a spectrum from transparent "glass box" models to opaque "deep neural networks." It then explores solutions, highlighting model-agnostic methods such as permutation feature importance as universal tools to understand complex models by shuffling input features and observing the impact on performance. Crucially, the explainer emphasizes that the foundation of any effective AI model is clean, well-understood data, asserting that "garbage in, garbage out" applies to explainability as much as performance. Ultimately, it demonstrates how visualization techniques can transform black boxes into transparent, ranked lists of priorities, enabling users to "see" how AI models "think" and fostering essential trust in an increasingly AI-driven world.


**** Learning Machine Learning ****


** Listen **

Podcast: Architectural Blueprints—The “4+1” View Model of Machine Learning

https://bit.ly/3GON12Q


** Read **

Architectural Blueprints—The “4+1” View Model of Machine Learning

https://bit.ly/40L5Nib


** See **

Interpretability/Explainability: “Seeing Machines Learn”

https://bit.ly/3H1kCGP


*** Next, read the "Data Science Approaches to Data Quality: From Raw Data to Datasets" article at

https://bit.ly/4lWnGDm