Description
Imagine a doctor prescribing a medication without explaining the reasoning or a judge delivering a verdict without justification. This lack of transparency can erode trust and hinder understanding. Similarly, in AI, many models’ “black box” nature can raise concerns about their decision-making processes.
Explainability methods provide the tools to open this black box, revealing the why behind AI predictions. Here is an overview of implementing these methods, empowering you to understand, validate, and trust your AI models. From simple techniques to advanced approaches, you’ll learn to shed light on your model’s inner workings and build more transparent AI systems.
Kognition.Info paid subscribers can download this and many other How-To guides. For a list of all the How-To guides, please visit https://www.kognition.info/product-category/how-to-guides/