ISO 26324:2022 Information and documentation — Digital object identifier system
Technical Committee: ISO/TC 46/SC 9 Identification and description
“This document specifies the syntax, description and resolution functional components of the digital object identifier system. It specifies the general principles for the creation, registration and administration of DOI names (where DOI is an initialism for “digital object identifier”). This document defines the syntax for a DOI name, which is used for the identification of an object of any material form (digital or physical) or an abstraction (such as a textual work) where there is a functional need to distinguish it from other objects. The DOI name does not replace, nor is it an alternative for, an identifier used in another scheme, such as the schemes defined by ISO/TC 46/SC 9. This document describes how the DOI system can be used in conjunction with another identifier scheme (for example, to provide additional functionality, such as resolution, where this is not already available), and how the character string of that other scheme can be integrated into the DOI system through the DOI metadata record or the DOI syntax or both. This document does not specify particular technologies to implement the syntax, description and resolution functional components of the digital object identifier system.”
ISO/IEC/IEEE 32675:2022 Information technology — DevOps — Building reliable and secure systems including application build, package and deployment
Technical Committee: ISO/IEC JTC 1/SC 7 Software and systems engineering
"This document provides requirements and guidance on the implementation of DevOps to define, control, and improve software life cycle processes. It applies within an organization or a project to build, package, and deploy software and systems in a secure and reliable way. This document specifies practices to collaborate and communicate effectively in groups including development, operations, and other key stakeholders. This document applies a common framework for software life cycle processes, with well-defined terminology. It contains processes, activities, and tasks that are to be applied to the full life cycle of software systems, products, and services, including conception, development, production, utilization, support, and retirement. It also applies to the acquisition and supply of software systems, whether performed internally or externally to an organization. These life cycle processes are accomplished through the involvement of stakeholders, with the ultimate goal of achieving customer satisfaction. The life cycle processes of this document can be applied concurrently, iteratively, and recursively to a software system and incrementally to its elements."
ISO/IEC TR 24368:2022 Information technology — Artificial intelligence — Overview of ethical and societal concerns
Technical Committee: ISO/IEC JTC 1/SC 42 Artificial intelligence
“This document provides a high-level overview of AI ethical and societal concerns.
In addition, this document provides information in relation to principles, processes and methods in this area; is intended for technologists, regulators, interest groups, and society at large; is not intended to advocate for any specific set of values (value systems). This document includes an overview of International Standards that address issues arising from AI ethical and societal concerns. […] Artificial intelligence (AI) has the potential to revolutionise the world and carry a plethora of benefits for societies, organizations and individuals. However, AI can introduce substantial risks and uncertainties. Professionals, researchers, regulators and individuals need to be aware of the ethical and societal concerns associated with AI systems and applications.
Potential ethical concerns in AI are wide ranging. Examples of ethical and societal concerns in AI include privacy and security breaches to discriminatory outcomes and impact on human autonomy. Sources of ethical and societal concerns include but are not limited to: unauthorized means or measures of collection, processing or disclosing personal data; the procurement and use of biased, inaccurate or otherwise non-representative training data; opaque machine learning (ML) decision-making or insufficient documentation, commonly referred to as lack of explainability; lack of traceability; insufficient understanding of the social impacts of technology post-deployment. AI can operate unfairly particularly when trained on biased or inappropriate data or where the model or algorithm is not fit-for-purpose. The values embedded in algorithms, as well as the choice of problems AI systems and applications are used for to address, can be intentionally or inadvertently shaped by developers’ and stakeholders’ own worldviews and cognitive bias.”
“The Accessibility Guidelines Working Group (AG WG) invites implementations of the Candidate Recommendation Snapshot of Web Content Accessibility Guidelines (WCAG) 2.2. Please see status and updates in What’s New in WCAG 2.2 Draft. Please submit implementations and any comments by 4 October 2022.”