Artificial Intelligence

The 1st IEEE International Conference on Engineering Reliable Autonomous Systems (ERAS) was held on May 29-30, 2025 in the USA (Worcester, Massachusetts). ERAS offered a space for all stakeholders across autonomous system reliability to come together to discuss the key challenges and present progress made towards solving them. Along with the presentations of the original, selected, peer-reviewed papers, three keynote talks from Academia, Industry, and Government each, were invited. The conference also hosted one tutorial delivered by IBM, and one workshop organized by MIT, MA, USA.

Are you an expert in AI System accuracy and/or robustness? Then join CEN/CENELEC JTC 21, WG 4 Foundational and Societal Aspects or WG 3 Engineering Aspects!

The IETF draft titled "Artificial Intelligence (AI) for Network Operations" (https://datatracker.ietf.org/doc/draft-king-rokui-ainetops-usecases/) explores how AI and machine learning (ML) can be integrated into network operations—a concept referred to as AINetOps. The primary aim is to automate and optimize network management tasks, thereby improving efficiency, reliability, and scalability. This approach is relevant to both single-layer (IP or Optical) and multi-layer (IP/Optical) networks and is intended to tackle the growing complexity of modern network infrastructures.

I am proud to have contributed as a StandICT fellow call 4 to the working draft of the prEN AI Trustworthiness Framework standard, produced by CEN/CENELEC, JTC 21, WG 4 Societal and Foundational Aspects, Task Group 3 (WI=JT021008).

The working draft was circulated to experts of the national standardization bodies on November 12th 2024 (WD prEN AI trustworthiness framework (Doc. N 830)).

Please provide your comments as a national expert on the WD prENAI Trustworthiness Framework standard by December 10th 2024.

AI systems can have positive impacts but at the same time they can also bring risks to safety, health and fundamental rights.

The AI Act, art. 9 requires that high-risk AI Systems are subject to a risk management system.

AI systems can have positive impacts but at the same time they can also bring risks to safety, health and fundamental rights.

The AI Act, art. 9 requires that high-risk AI Systems are subject to a risk management system.

As AI is omnipresent and impacting everyones life, ensuring that AI systems are trustworthy is quintessential.

The AI Act is a European regulation on artificial intelligence (AI) promoting the uptake of human-centric and trustworthy artificial intelligence (AI), while ensuring a high level of protection of health, safety, fundamental rights. One way for companies to prove conformity with the AI Act is to meet the underlying harmonized standards.

The EN AI Trustworthiness Framework is one of these harmonized standards.

In today's rapidly evolving digital landscape, the integration of Artificial Intelligence (AI) into 5G and beyond networks has reached a critical juncture. While the potential of such integration offers great opportunities for innovation, efficiency, and service enhancement, it is not without its challenges. The primary obstacle lies in the complexity of the underlying network infrastructure, compounded by the lack of standardized guidelines for AI integration.

Progress in the harmonization of clinical information interoperability standards such as HL7 FHIR, ISO 13606, and ISO 13940 (as the ISO 24305 project does) should be joined with new AI standards such as the reference framework (next versions for ISO/IEC23053) and ISO/IEC AWI TR 18988 "Artificial intelligence - Application of AI technologies in health informatics," as well as for the security and effective use of standards for language models in the Electronic Health Record.

I've been involved in contributing to the development of standards (International, Regional & National) for over ten years.

Recently, its become evident that Artificial Intelligence (AI) could have an impact on the standards development process and procedures.  A list of issues, and where available, the resolution of those issues, would be a useful resource within all Standard Development Organisations (SDO).

Please find below an extract, derived from a paper written by AI ethicist Alessio Tartaro

Recall and withdrawal of AI systems

According to the AI Standardization Request from the European Commission to CEN/CENELEC, European standards or standardisation deliverables shall provide suitable organisational and technical solutions to ensure that AI systems are resilient against attempts to alter their use, behaviour, or performance or compromise their security properties, by malicious third parties, exploiting vulnerabilities of AI systems.

Check out this position paper on the needs, benefits and risks of NLP standardization: Is NLP Ready for Standardization?

Part of this work has been conducted through a StandICT Fellowship.

Teaser: Both CEN-CENELEC and ISO-IEC are currently taking new steps towards NLP standardization, with more groups and more projects about to start. More news to come in the upcoming weeks!

This DIN SPEC is currently under development with an expected publication date in September 2023.

For further information see Business plans (din.de) or feel free to reach out to me.

What would be the requirements of an AI algorithm service to be made available to the public?

Answer (anonymously) these 3 questions here https://algorithmclassification.org/

1.- What would you make you trust an algorithm applied to your data?

2.- What is your major concern about an algorithm applied to your data?

3.- What would be a possible solution to trust AI algorithms?

Thanks

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My project has the goal to provide an important basis for further work on standards that incorporate in particular ethical considerations. The focus of the project lies on one particular application of AI, namely automated vehicles (AVs). As the acceptance and use of AVs is contingent upon the trust people build in this technology, it is of utmost importance that technology is developed that is human-centred and trustworthy. At the same time this should also be reflected in the development of the standards on AVs.

ISO/IEC CD 5392 Information technology — Artificial intelligence — Reference architecture of knowledge engineering

Under development

https://www.iso.org/standard/81228.html

AI (and more specifically ML) can produce hard-to-explain/understand outputs (e.g., non-linear prediction functions), thus increasing the "black-box" perception that humans have about these systems. To address that, there is an ongoing discussion on the integration of simulators and digital twins to assist the operation of AI systems. The usage of simulators is expected to improve the trustworthiness and reliability of AI. However, there are still a lot of questions to be addressed and a lot of standardization work to be done. 

Dear colleagues,

The IEEE standard on the Fuzzy Markup Language, is under revision. 

Several extensions are under development to include other systems, e.g., type-2 , learning.

Abstract of the previous standard:

Dear colleagues,

The IEEE SA, has an ongoing work onfor XAI – eXplainable Artificial Intelligence

Abstract of the work:

"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."

Please consider to join the effort: https://sagroups.ieee.org/2976/

Dear all,

The standard was published at 12th May 2022.

Can be found at:

https://standards.ieee.org/ieee/1872.2/7094/

Dear Colleagues,

If it would be of interest to anyone dealing with AI, especially applied to smart energy, I am looking for a collaboration working on standards in applying AI to smart energy and particularly to smart PV systems.

MPAI has published a book entitled: “Towards Pervasive and Trustworthy Artificial Intelligence: How standards can put a great technology at the service of humankind”.

With the printing industry sparing no efforts publishing books on Artificial Intelligence (AI), why should there be another that, in its title and subtitle, combines the overused words “AI” and “trustworthy”, with the alien words “standards” and “pervasive”?

Use of technologies based on Artificial Intelligence (AI) is extending to more and more applic­ations yielding one of the fastest-grow­ing markets in the data analysis and service sector.

However, industry must overcome hurdles for stakeholders to fully exploit this historical oppor­tunity: the current framework-based development model that makes applic­ation redep­loyment difficult, and monolithic and opaque AI applications that generate mistrust in users.

The Open Ethics Transparency Protocol (OETP) describes the creation and exchange of voluntary ethics Disclosures for IT products. It is brought as a solution to increase the transparency of how IT products are built and deployed. The scope of the Protocol covers Disclosures for systems such as Software as a Service (SaaS) Applications, Software Applications, Software Components, Application Programming Interfaces (API), Automated Decision-Making (ADM) systems, and systems using Artificial Intelligence (AI).

IEEE 7007 WG created a unique standard that will contribute to the development of new technologies ethically aligned to human values. The IEEE 7007 Standard has an ontological representation which facilitates the investigation of the domain; and a formal language that adds precision to the knowledge and data collected during this investigation. The nature of the ontologies allows this ontological representation to be used in a wide variety of applications across all AIS domain.

The EU-funded, StandICT.Eu 2023  Project, ICT Standardisation Observatory and Support Facility in Europe,  has just published the first of a series of Landscape Reports on ICT standards, Landscape Of AI Standards

Edge AI is rapidly gaining momentum with growing investments and awareness of its benefits, such as reducing costs and latency times for improved user experience and increased levels of security in terms of data privacy through local processing. 

In a joint project with the Federal Ministry of Economic Affairs and Energy (BMWi), DIN and DKE developed together with experts from industry, science, the public sector and civil society a roadmap for standards and specifications in the field of artificial intelligence. The aim was the early development of a framework for action in the field of standardization which will strengthen the global competitiveness of German industry and make European values the global benchmark. With this step, DIN is implementing the AI strategy of the German Federal Government.

The EU Observatory For ICT Standards WG-AI

Great effort in the last few months by the EUOS WG on Artificial Intelligence chaired by Lindsay Frost (NEC, ETSI) has already produced a draft Technology Landscape Research Report for the European Commissions DG JRC (joint Research Centre) on standardization related to AI Risk.

The ICT Standardization landscape is an every changing, living and dynamic ecosystem of ecosystems where new technologies, tools, techniques, components, products and services are being innovated and disrupted on a constant basis. To remain current regular standardization landscape research exercises need to take place to capture the latest state-of-the-art.