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Test specifications with a set of indicators for common corruption and adversarial attacks, which can be used to evaluate the robustness of artificial intelligence-based image recognition services are provided in this standard. Robustness attack threats and establishes an assessment framework to evaluate the robustness of artificial intelligence-based image recognition service under various settings are also specified in this standard.

This recommended practice establishes an evaluation framework for the capabilities of artificial intelligence dialogue systems such as chatbots, consulting terminals, or operation interfaces. The recommended practice defines and classifies the types and levels of the intelligence capabilities according to a checklist of criteria. The checklist tables describe the criteria used to determine the level that a dialogue system achieves based on the analysis of behavior and performance.

The standard defines specific terminology utilized in artificial intelligence and machine learning (AI/ML). The standard provides clear definition for relevant terms in AI/ML. Furthermore, the standard defines requirements for data formats.

This standard provides stakeholders with certifiable and responsible child and student data governance methodologies.

This standard defines mandatory and optional requirements and constraints that need to be satisfied for an AI method, algorithm, application or system to be recognized as explainable. Both partially explainable and fully or strongly explainable methods, algorithms and systems are defined. XML Schema are also defined.

This standard defines attributes related to industrial Artificial Intelligence (AI) data that facilitates the classification, association, and mapping towards value creation using data. The attributes include but are not limited to data source, type, ownership, sampling frequency, traceability, privacy attributes for modeling, sampling, shareability and its use in AI algorithms.

A set of operator interfaces frequently used in artificial intelligence (AI) applications is defined in this standard, where the AI operators refer to the standard building blocks and primitives for performing basic AI operations. The functionality and the specific input and output operands of an AI operator are discussed, as well as both generality and efficiency. Various types of operators, such as those related to basic mathematics, neural network, and machine learning, are highlighted.

Efficient coding tool sets for compression, decompression, and reconstructing of the immersive visual content data is provided. The target applications and services include, but are not limited to, virtual reality (VR), such as unmanned aerial vehicle-based VR, augmented reality, panorama video, free-view TV, panoramic stereo video, and other video-/audio-enabled services and applications, such as immersive video streaming, broadcasting, storage, and communication.

This standard provides a general shared function model for cloud computing, in order to normalize how functions are shared between cloud service providers (CSPs) and cloud service customers (CSCs). The standard specifies functions ownership from seven aspects for three main cloud service delivery models, including Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). The seven aspects of function are as follows:1) The physical infrastructure function.2) The virtualization infrastructure function.3) The operating system function.4) The network control function.5) The application function.6) The data function.7) The identity and access management function. Each of the seven aspects considers many factors including security, management, etc. In this standard, levels 1 to 4, which are clearly the service provider's function for IaaS, PaaS, and SaaS, are briefly mentioned for the integrity of the model. This standard focuses on levels 5, 6, and 7.

A functional model for federation based on the NIST Cloud Federation Reference Architecture is defined in this standard. This model allows a range of deployment topologies and governance. As a general federation model, it can be applied to many application domains using different implementation approaches. As such, it includes cloud-to-cloud federation and interoperability.

Advice is given for cloud computing ecosystem participants (cloud vendors, service providers, and users) of standards-based choices in areas such as application interfaces, portability interfaces, management interfaces, interoperability interfaces, file formats, and operation conventions. These choices are grouped into multiple logical profiles, which are organized to address different cloud roles.

The requirements for a systems/software engineering process for privacy-oriented considerations regarding products, services, and systems utilizing employee, customer, or other external user's personal data are defined by this standard. Organizations and projects that are developing and deploying products, systems, processes, and applications that involve personal information are candidate users of the IEEE 7002 standard. Specific procedures, diagrams, and checklists are provided for users of the IEEE 7002 standard to perform conformity assessments on their specific privacy practices. Privacy impact assessments (PIAs) are described as a tool for both identifying where privacy controls and measures are needed and for confirming they are in place.