Explainable AI - Anchor Explanations
Anchor explanations are a type of explanation method used in the field of explainable artificial intelligence (XAI). The purpose of anchor explanations is to provide transparent, interpretable, and understandable reasons for the predictions made by machine learning models.
An anchor explanation is a specific rule or condition that, when met, would cause the model to make the same prediction. The rule or condition is presented in a human-readable format, which makes it easy for end-users to understand how the model arrived at its prediction.
Consider a machine learning model used for credit risk assessment. An anchor explanation for a prediction that a particular individual is a high-risk borrower might be "the model predicted that the borrower is high-risk because they have a history of missed payments on their previous loans." This explanation helps the end-user understand why the model made that particular prediction, and allows them to make informed decisions based on that information.
The anchor explanation method is designed to be model-agnostic, meaning it can be used with any type of machine learning model. This makes it a useful tool for a wide range of applications, including healthcare, finance, and natural language processing.
Tags: explainable-ai anchor-explanations machine-learning credit-risk transparency interpretability model-agnostic healthcare finance natural-language-processing predictions end-users rule-based xai