Ultimate Guide to Model Explainability: Anchors. There is now a laundry list of Machine Learning and Deep Learning algorithms to solve each AI problem.
There is now a laundry list of Machine Learning and Deep Learning algorithms to solve each AI problem. The more complex a model, the more accurate it tends to be in general (of course, if it has not been over-fit, and if the data pipeline is fine, and so on).
Although having higher accuracy of prediction is desirable, it is also becoming increasingly important to be able to explain the behaviour of the model. This is especially true in light of the new GDPR regulations which offer the “Right to Explanation.” This means that if anyone uses an AI model to make predictions for someone, then they are liable to explain why the model has predicted so. More importantly in case of classification problems where mis-classification can have high costs.
Understanding how to build AI models is one thing. Understanding why AI models provide the results they provide is another. Even more so, explaining any type of understanding of AI models to humans is yet another challenging layer that must be addressed if we are to develop a complete approach to Explainable AI.
Explaining Your Machine Learning Models with SHAP and LIME! Helping you to demystify what some people might perceive as a “black box” for your machine learning models.
Modern machine learning architectures are growing increasingly sophisticated in pursuit of superior performance, often leveraging black box-style architectures which offer computational advantages at the expense of model interpretability.
Explainable AI is more important for those processes where understanding the process of getting prediction is more important than just getting higher accuracy.
Five critical questions to explain Explainable AI. Getting started on your Responsible AI journey