Improving system interpretability and explainability with data analysis can be achieved through various techniques such as feature importance analysis, model visualization, and model-agnostic ...

Discover effective strategies for balancing model interpretability with accuracy in data science, ... Use models with built-in explainability features, like GAMs.

When we were working with child welfare screeners, for example, we retrained the model using only features that met our definitions for interpretability, and the performance decrease was almost ...

An example of explainability in an AI algorithm might be a machine learning model used in credit scoring, where the AI evaluates an individual’s creditworthiness based on various factors like ...

The interactive attention graph convolution network (IAGCN), a novel model proposed in this article, will revolutionize aspect-level sentiment analysis (SA). IAGCN effectively addresses these key ...

Overall, this review concludes that post-model interpretability methods are the most widely used type of interpretability in smart grid related ML works. According to the identified need for a quick ...

To many AI practitioners and consumers, explainability is a precondition of AI use. A model that, without showing its work, tells a doctor what medicine to prescribe may be mistrusted. No ...