Research and analysis

As individuals and communities interact in and with an environment that is increasingly virtual they are often vulnerable to the commodification of their digital exhaust. Concepts and behavior that are ambiguous in nature are captured in this environment, quantified, and…

In this paper, we make the case that interpretability and explainability are distinct requirements for machine learning systems. To make this case, we provide an overview of the literature in experimental psychology pertaining to interpretation (especially of numerical stimuli) and…

We introduce four principles for explainable artificial intelligence (AI) that comprise fundamental properties for explainable AI systems. We propose that explainable AI systems deliver accompanying evidence or reasons for outcomes and processes; provide explanations that are understandable to individual users;…

NIST is developing a framework to better manage risks to individuals, organizations, and society associated with artificial intelligence (AI). The NIST Artificial Intelligence Risk Management Framework (AI RMF or Framework) is intended for voluntary use and to improve the ability…

This NIST Interagency/Internal Report (NISTIR) is intended as a step toward securing applications of Artificial Intelligence (AI), especially against adversarial manipulations of Machine Learning (ML), by developing a taxonomy and terminology of Adversarial Machine Learning (AML). Although AI also includes…

What is this report about? The Virtual and Augmented Reality industries represent a very substantial and fast-growing opportunity. According to Goldman Sachs, AR and VR are expected to grow into a $95 billion market by 2025. Against this backdrop, there’s…