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.
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.
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.
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.
This standard defines a trusted data circulation platform based on blockchain and distributed ledger technologies. The system overview of trusted data circulation platform is defined including underlying computation engine layer, blockchain and DLT core function layer, trusted data circulation layer, and interface layer. The functional modules, data circulation processes, technical and security requirements are specified. This standard includes recommendations for controlling the allowable purposes and amount of data utilization, and for data privacy and resistance to tampering during the relevant data operations.
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.
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.
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.
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.
A set of processes by which organizations seek to make their services age appropriate is established in this standard. The growing desire of organizations to design digital products and services with children in mind and reflects their existing rights under the United Nations Convention on the Rights of the Child (the Convention) is supported by this standard. While different jurisdictions may have different laws and regulations in place, the best practice for designing digital services that impact directly or indirectly on children is offered by this standard. It sets out processes through the life cycle of development, delivery and distribution, that will help organizations ask the right relevant questions of their services, identify risks and opportunities by which to make their services age appropriate and take steps to mitigate risk and embed beneficial systems that support increased age appropriate engagement. One in three users online is under 18, which means that this standard has wide application.
This recommended practice produces best practices for meeting the requirements of IEEE P7004: Standard for Child and Student Data Governance, when designing, provisioning, configuring, operating, and maintaining an online virtual classroom experience for synchronous online learning, education, and training. The recommended practice includes language that can be referenced in requests for proposals (RFPs) for online (also known as virtual) classroom solutions, the operational runbook(s) for such solutions, and the assessment and certification guideline(s) for compliance process of such solutions.
This recommended practice provides recommendations for next steps in the application of IEEE Std 7010, applied to meeting Environmental Social Governance (ESG) and Social Development Goal (SDG) initiatives and targets. It provides action steps and map elements to review and address when applying IEEE Std 7010. This recommended practice serves to enhance the quality of the published standard by validating the design outcomes with expanded use. It provides recommendations for multiple users to align processes, collect data, develop policies and practices and measure activities against the impact on corporate goals and resulting stakeholders. This recommended practice does not set metrics for measurement and/or reporting, but rather identifies well recognized indicators to consider in assessment and measurement of progress.