Standard

This standard defines a model for ethical considerations and practices in the design, creation and use of empathic technology, incorporating systems that have the capacity to identify, quantify, respond to, or simulate affective states, such as emotions and cognitive states. This includes coverage of 'affective computing', 'emotion Artificial Intelligence' and related fields.

The standard identifies/addresses the manner in which personal privacy terms are proffered and how they can be read and agreed to by machines.

This document is the first of a family of standards. It specifies a set of functions for computer graphics programming, the graphical kernel system. Provides functions for two dimensional graphical output, the storage and dynamic modification of pictures, and operator input. Applicable to a wide range of applications that produce two dimensional pictures on vector or raster graphical devices in monochrome or colour.

A set of processes by which organizations can include consideration of ethical values throughout the stages of concept exploration and development is established by this standard. Management and engineering in transparent communication with selected stakeholders for ethical values elicitation and prioritization is supported by this standard, involving traceability of ethical values through an operational concept, value propositions, and value dispositions in the system design. Processes that provide for traceability of ethical values in the concept of operations, ethical requirements, and ethical risk-based design are described in the standard. All sizes and types of organizations using their own life cycle models are relevant to this standard.

The impact of artificial intelligence or autonomous and intelligent systems (A/IS) on humans is measured by this standard. The positive outcome of A/IS on human well-being is the overall intent of this standard. Scientifically valid well-being indices currently in use and based on a stakeholder engagement process ground this standard. Product development guidance, identification of areas for improvement, risk management, performance assessment, and the identification of intended and unintended users, uses and impacts on human well-being of A/IS are the intents of this standard.

Guidance to developers of IEEE 1232 - conformant applications is provided in this guide. A simple doorbell is used as an example system under test to illustrate how the static model constructs of Artificial Intelligence Exchange and Service Tie to All Test Environments (AI-ESTATE) are used to form a diagnostic reasoner knowledge base. Each of AI-ESTATE's knowledge base types is discussed in conceptual terms, and how those concepts are represented in exchange files is shown. Also, some of the nuanced aspects of diagnostic knowledge bases in AI-ESTATE are clarified. An example reasoner session is provided to illustrate the use of AI-ESTATE services.

A framework of knowledge graphs is proposed in this standard. The knowledge graph conceptual model, construction and integration process of knowledge graphs, main activities in the processes, and stakeholders of knowledge graphs are described in detail. This standard can be applied in various organizations that plan, design, develop, implement, and apply knowledge and in organizations that develop support technologies, tools, and services to knowledge graphs.

Artificial intelligence (AI) computing differs from generic computing in terms of device formation, operators, and usage. AI server systems, including AI server, cluster, and high-performance computing (HPC) infrastructures are designed specifically for this purpose. The performance of these infrastructures is important to users not only on generic models but also on the ones for specific domains. Formal methods for the performance benchmarking for AI server systems are provided in this standard, including approaches for test, metrics, and measure. In addition, the technical requirements for benchmarking tools are discussed.

The AI development interface, AI model interoperable representation, coding format, and model encapsulated format for efficient AI model inference, storage, distribution, and management are discussed in this standard.

Federated machine learning defines a machine learning framework that allows a collective model to be constructed from data that is distributed across repositories owned by different organizations or devices. A blueprint for data usage and model building across organizations and devices while meeting applicable privacy, security and regulatory requirements is provided in this guide. It defines the architectural framework and application guidelines for federated machine learning, including description and definition of federated machine learning; the categories federated machine learning and the application scenarios to which each category applies; performance evaluation of federated machine learning; and associated regulatory requirements.

This recommended practice describes ethical considerations and recommended best practices in the design of artificial intelligence as used by adaptive instructional systems. The ethical considerations derived from P2247.1, Standard for the Classification of Adaptive Instructional Systems, is directly related to: P2247.1 Standard for the Classification of Adaptive Instructional Systems, P2247.2 Interoperability Standards for Adaptive Instructional Systems (AISs), and P2247.3 Recommended Practices for Evaluation of Adaptive Instructional Systems.

This recommended practice specifies governance criteria such as safety, transparency, accountability, responsibility and minimizing bias, and process steps for effective implementation, performance auditing, training and compliance in the development or use of artificial intelligence within organizations.