As if sand were stone. New concepts and metrics to probe the ground on which to build trustable AI
Abstract
Background We focus on the importance of interpreting the quality of the labeling used as the input of predictive models to understand the reliability of their output in support of human decision-making, especially in critical domains, such as medicine. Methods Accordingly, we propose a framework distinguishing the reference labeling (or Gold Standard) from the set of annotations from which it is usually derived (the Diamond Standard). We define...
Paper Details
Title
As if sand were stone. New concepts and metrics to probe the ground on which to build trustable AI
Published Date
Sep 11, 2020
Volume
20
Issue
1
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