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 ...

Molnar distinguishes among Algorithm Transparency, Global Holistic Model Interpretability, Global Model Interpretability on a Modular Level, Local Interpretability for a Single Prediction, and ...

Here are three reasons why explainability matters: People need to understand not just outcomes, but details behind the model (data provenance, performance metrics, etc.) to have confidence in the ...

Model interpretability refers to the extent to which a human can understand the cause of a decision made by an AI model. In simple terms, it’s about opening the "black box" of AI to see how and ...

Transparency and clear explanations about AI sourcing, machine learning, algorithms, language models, and evolving AI technologies support trustworthiness. From a technical perspective, interpretable ...

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 ...

A new explainable AI technique transparently classifies images without compromising accuracy. The method, developed at the ...

Explainability or interpretability in the context of AI is the extent to which an AI company can explain their model’s internal processes and decisions in human terms.