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Standardization empowering AI-enabled systems in healthcare – workshop report
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Towards auditable AI systems
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Trust and artificial intelligence
The artificial intelligence (AI) revolution is upon us, with the promise of advances such as driverless cars, smart buildings, automated…
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Psychological foundations of explainability and interpretability in artificial intelligence
In this paper, we make the case that interpretability and explainability are distinct requirements for machine learning systems. To make…
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Four principles of explainable artificial intelligence
We introduce four principles for explainable artiļ¬cial intelligence (AI) that comprise fundamental properties for explainable AI systems. We propose that…
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A taxonomy and terminology of adversarial machine learning
This NIST Interagency/Internal Report (NISTIR) is intended as a step toward securing applications of Artificial Intelligence (AI), especially against adversarial…
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Towards a standard for identifying and managing bias in artificial intelligence
As individuals and communities interact in and with an environment that is increasingly virtual they are often vulnerable to the…
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U.S. leadership in AI: a plan for federal engagement in developing technical standards and related tools
NIST has released a plan for prioritizing federal agency engagement in the development of standards for artificial intelligence (AI). The…
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AI risk management framework
NIST is developing a framework to better manage risks to individuals, organizations, and society associated with artificial intelligence (AI). The…
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Artificial intelligence standardization white paper (2018 edition)
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An artificial intelligence standards roadmap: making Australiaās voice heard
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