FAIR Framework for Physics-Inspired AI in High Energy Physics (Final Technical Report)
Abstract
The findable, accessible, interoperable, and reusable (FAIR) data principles provide a framework for examining, evaluating, and improving how data is shared to facilitate scientific discovery.Generalizing these principles to research software and other digital products is an active area of research.Machine learning (ML) models -algorithms that have been trained on data without being explicitly programmed --and more generally, artificial intelligence (AI) models, are an important target for this because of the ever-increasing pace with which AI is transforming scientific domains, such as experimental high energy physics (HEP).In this paper, we propose a practical definition of FAIR principles for AI models in HEP and describe a template for the application of these principles.We demonstrate the template's use with an example AI
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