Overview

Machine learning has many limitations and lacks fundamental security standards. Interest is growing across academic researchers as well as industry professionals who all aim to answer the same question: how do we build and deploy machine learning models that are robust, explainable, unbiased, privacy-preserving, and ultimately trustworthy? To address this core issue, a framework was built at Idaho National Laboratories that outlines standards for secure machine learning development. These machine learning pillars provided a basis and guiding methodology for the direction and design of this research, which addresses each of the pillars but focuses on four central data science topics: data types, sourcing, management, and validation.

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