Artificial Intelligence

A new standard for AI-based Network Applications in beyond 5G

Alexandru Vulpe — Thu, 28 Mar 2024 12:28:48 +0000

A new standard for AI-based Network Applications in beyond 5G Alexandru Vulpe Thu, 03/28/2024 - 13:28

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. This has resulted in fragmented solutions that hinder interoperability, scalability, and security, ultimately slowing down the deployment of next-generation network applications and limiting their potential impact across various sectors.

The necessity for a standardized approach cannot be overstated. The absence of a unified framework for AI integration in 5G and beyond networks poses a significant barrier to progress. A standard is needed to simplify the network infrastructure complexity, ensure interoperability across different systems and devices, accelerate service creation and deployment timelines, and optimize the utilization of network resources. Also, with the exponential increase in digital threats, a standard is critical for enhancing the security and resilience of network applications. It is also important as it would facilitate cost-effective service deployments, unlocking innovation potential, and ensuring that the technological advancements are accessible and beneficial to all stakeholders.

Recognizing the pressing need for a solution, I proposed the development of the new IEEE P1948 Standard for AI-based Network Applications in 5G and beyond. This initiative is aimed at establishing harmonized guidelines and protocols that would address the current gaps in AI integration within network infrastructures. My work involved extensive research to identify the core areas of focus, collaboration with industry experts to gather insights and feedback, and leading discussions within the COM/AccessCore-SC/NAB5G Working Group to draft the initial standard framework.

The PAR (Project Authorization Request) for the development of the standard will be discussed at the next New Standards Committee (NesCom) meeting in May 2024, with an expected date for completing the standard and going to the balloting process in early 2025

Addressing clinical information interoperability standards with AI standards to increase its power in its application in health.

Carlos Luis Parra-Calderón — Sun, 11 Feb 2024 18:08:23 +0000

Addressing clinical information interoperability standards with AI standards to increase its power in its application in health. Carlos Luis Pa… Sun, 02/11/2024 - 19:08

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.

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AI Use in the Standards Making Process

Paull Ferris — Fri, 12 Jan 2024 16:15:06 +0000

AI Use in the Standards Making Process Paull Ferris Fri, 01/12/2024 - 17:15

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).

If anyone has thoughts on how such a list could be organised and how best to format its contents, do contribute. There are also rapidly developing standards in this area, but I'm not aware of any work developing a standard on 'How best to develop a Standard in an AI world'. If there is such a thing, or if anyone knows of work ongoing in this area, please contribute.

My work has been in developing standards by consensus among nominated experts. Other approaches and SDOs may be presented with different AI challenges, which would be equally valuable.

In the next couple of days, I'll post some ideas on organisation and classification, and also some issues both 'resolved' (hopefully) and those that require some further thought. The sort of issues that have raised questions to date exist around the standards creation lifecycle, the publication and use of standards by AI systems, the market perception of standards where AI cites existing standards, the cultural issues raised by AI applying standards outside their 'home' jurisdiction and more. Specific challenges include management of Intellectual Property (IP), generation of an initial 'Working Draft', comments received during editorial work, finalising the draft standards, declarations of AI content, auditing of content contributed. There are sure to be others.

What tags should this post use?

Paul

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Recall and Withdrawal of AI systems

Viveka Bonde — Fri, 29 Dec 2023 10:00:48 +0000

Recall and Withdrawal of AI systems Viveka Bonde Fri, 12/29/2023 - 11:00

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

Recall and withdrawal of AI systems

In instances where the risk level associated with an AI system is classified as unacceptable, organizations should initiate procedures for recall or withdrawal, whether temporary or permanent. This course of action is pivotal in preventing the occurrence of adverse effects. These actions should be considered as measures of last resort [1].

The guidance is an adaptation of the general guidelines provided by ISO 10393:2013 - Consumer Product Recall - Guidelines for Suppliers, specifically tailored to address the unique challenges and considerations pertaining to AI systems.

1 General recommendations

1.1 General

Organizations should establish a structured recall management system for AI systems, which includes the formation of a recall management team, delineation of processes, and allocation of necessary resources. The system should ensure preparedness for recall events, encompassing the entire lifecycle of AI systems.

1.2 Policy

Each organization should develop and implement a recall policy for AI systems that specifies the conditions under which a recall will be initiated. The policy should define the roles and responsibilities of personnel, expected outcomes, and the framework for risk assessment and communication strategies during a recall event.

1.3 Documentation and record keeping

Organizations should maintain comprehensive documentation that records all aspects of the AI system's design, development, deployment, monitoring, and maintenance activities. Records should include, among others, data on performance metrics, ethical compliance assessments, user feedback, and detailed accounts of any decisions and actions taken in the event of a recall.

1.4 Expertise required to manage a recall

Organizations should ensure that personnel involved in the recall process have the necessary expertise, which includes, among others, knowledge of AI technology, risk management, legal and ethical compliance, and communication. Appropriate training should be provided to ensure that the team can identify potential risks and execute recall measures effectively.

1.5 Authority for key decision

The organization should designate individuals with the authority to make critical decisions during a recall. This includes the initiation of a recall, stakeholder communication, and the termination of the AI system's deployment. The chain of command and decision-making protocols should be clearly established and communicated to all relevant personnel.

1.6 Training and recall simulation Organizations should conduct regular training and recall simulation exercises to ensure that personnel are prepared to execute the recall policy effectively. Training should cover all aspects of the recall process, from risk identification to communication with stakeholders and the restoration of service post-recall.

2 Assessing the need for an AI system recall

2.1 General

Organizations should establish clear guidelines for the initiation of an assessment process to determine the necessity of an AI system recall. These guidelines should outline the triggers for assessment initiation, such as performance anomalies, ethical breaches, user complaints, or regulatory inquiries. The assessment process should be systematic and commence promptly upon the identification of a potential issue that may warrant a recall.

2.2 Incident notification

Organizations should implement an incident notification protocol to promptly inform all relevant parties, including regulatory bodies, stakeholders, and affected users, of a potential issue that could lead to a recall. The notification system should enable rapid dissemination of information and facilitate the immediate commencement of the assessment process.

2.3 Incident investigation

Upon notification of a potential incident, organizations should conduct a thorough investigation to ascertain the nature and severity of the issue. The investigation should involve collecting and analyzing data related to the incident, consulting with experts, and reviewing the AI system's operational history to identify any contributing factors or patterns.

2.4 Assess the risk

Organizations should employ a structured methodology to evaluate the risks associated with the identified issue. This assessment should consider the potential for harm to users, violations of ethical standards, legal non-compliance, and broader societal impacts. The risk assessment should guide the organization in determining the appropriate course of action.

2.5 Traceability

Organizations should maintain a traceability system for all AI systems to facilitate tracking and location throughout their operational lifecycle. Traceability measures should enable the organization to quickly identify all instances of the AI system in use, including deployment locations, responsible parties, and affected users.

2.6 Product recall decision

The organization should establish decision-making criteria for initiating a recall of an AI system. This should include a threshold for action based on the risk assessment, the potential impact of the recall, and the feasibility of corrective measures. The decision-making process should be documented, transparent, and involve key stakeholders to ensure that all relevant factors are considered.

3 Implementing an AI system recall

3.1 General

Organizations should develop a comprehensive recall implementation plan for AI systems, detailing the steps from initiation to completion. This plan should be activated upon the decision to recall and should include provisions for resource allocation, stakeholder communication, and measures to mitigate the impact of the recall.

3.2 Initiate the recall action

Once a decision to recall an AI system has been made, the organization should initiate the recall action as per the established plan. The initiation process should include the issuance of formal recall notices and activation of the recall management team. The scope and urgency of the recall should dictate the immediate actions taken.

3.3 Communication

Effective communication strategies should be employed to inform all affected parties, including users, partners, and regulatory bodies, about the recall. Information disseminated should clearly describe the reason for the recall, the actions required by the recipients, and the channels through which they can seek further information or support.

3.4 Implement the recall

The recall should be implemented according to the plan, with actions taken to cease the operation of the recalled AI system, inform affected users, and address the identified issue. If the recall involves a physical product, procedures should include the retrieval of the product from all distribution points. If it is a software-based or cloud-based AI system, the recall may involve remote deactivation, patching, rollback, user access restriction, and similar measures.

3.5 Monitor and report

The organization should continuously monitor the recall process to ensure compliance and effectiveness. Progress reports should be generated and communicated to stakeholders at regular intervals. Monitoring activities should also include tracking the recall's reach and verifying that corrective actions have been implemented

3.6 Evaluate effectiveness

Post-recall, the organization should evaluate the effectiveness of the recall process. This evaluation should assess whether the recall objectives were met, if the risk was mitigated, and how the recall impacted the stakeholders. The findings should be documented and used to inform future recalls.

3.7 Review and adjust recall strategy

Following the evaluation, the organization should review the recall strategy and make necessary adjustments to improve future responses. This review should consider feedback from stakeholders, the results of the effectiveness evaluation, and any changes in regulatory requirements or organizational policies.

4. Continual improvement of recall programme

4.1 General

Organizations should establish a framework for the continuous improvement of the recall program for AI systems. This framework should be based on the principles of iterative learning, feedback incorporation, and process optimization. It should aim to enhance the organization's ability to respond to recall situations effectively and efficiently.

4.2 Reviewing the recall

After the completion of a recall, the organization should conduct a comprehensive review of the recall process. This review should assess how the recall was executed, the efficacy of the communication strategies, the adequacy of the resources allocated, and the overall management of the recall. The review should identify both strengths and areas for improvement.

4.3 Corrective actions to prevent reoccurrence Based on the review, the organization should identify and implement corrective actions to address any deficiencies observed during the recall process. These actions should aim to prevent the reoccurrence of similar issues. The organization should also revise risk assessment and management strategies to incorporate lessons learned from the recall.

[1] Tartaro, Alessio (2023). When things go wrong: the recall of AI systems as a last resort for ethical and lawful AI. AI and Ethics. https://doi.org/10.1007/s43681-023-00327-z

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Cybersecurity for AI Systems

Francisco Medeiros-Filho — Wed, 20 Dec 2023 08:24:38 +0000

Cybersecurity for AI Systems Francisco Mede… Wed, 12/20/2023 - 09:24

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. Organisational and technical solutions shall include, where appropriate, measures to prevent and control cyberattacks trying to manipulate AI specific assets, such as training data sets (e.g. data poisoning) or trained models (e.g. adversarial examples), or trying to exploit vulnerabilities in AI system’s digital assets or in the underlying ICT infrastructure. These solutions shall be appropriate to the relevant circumstances and risks. Furthermore, the requested European standards or standardisation deliverables shall take due account of the essential
requirements for products with digital elements as listed in Annex I of the EC proposed Regulation on horizontal cybersecurity requirements for products with digital elements (CRA proposal of 15 September 2022).

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New work ahead on NLP standards

Lauriane Aufrant — Wed, 31 May 2023 17:30:16 +0000

New work ahead on NLP standards Lauriane Aufrant Wed, 05/31/2023 - 19:30

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!

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DIN SPEC 92005 Artificial Intelligence - Uncertainty quantification in machine learning

Lukas Höhndorf — Wed, 24 May 2023 12:30:10 +0000

DIN SPEC 92005 Artificial Intelligence - Uncertainty quantification in machine learning Lukas Höhndorf Wed, 05/24/2023 - 14:30

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.

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Answered By Francisco Mede…
11 months ago

I would suggest bringing this initiative into CEN/CENELEC JTC21. One NSB could propose a New Work Item.

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Classification of AI algorithms

Alberto Abella — Wed, 15 Mar 2023 00:47:06 +0000

Classification of AI algorithms Alberto Abella Wed, 03/15/2023 - 01:47

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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Data Labeling and OETP

Nikita Lukianets — Sun, 14 Aug 2022 13:03:29 +0000

Data Labeling and OETP Nikita Lukianets Sun, 08/14/2022 - 15:03

Disclosure of Data Labeling plays a key role in transparency of the models trained by subject-matter experts, or for a crowdsourced data labeling approaches.

Bring transparency to the systemic properties of the AI models by developing an Open Ethics Data “Passport” (https://openethics.ai/oedp/). The Data Passport has a purpose at depicting the origins of the training datasets by bringing a standardized approach to convey information about data annotation processes, data labelers profiles, and correct scoping of the labeler’s job. Data Passport is integral part of the Disclosures and will be accessible along with other information about autonomous systems.

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"Big Tech" is an important driver of innovation, however, the consequent concentration of power creates Big Risks for the economy, ethical use of technology, and basic human rights (we consider privacy as one of them).

A decentralization of SBOM (Software Bill of Materials) and data processing disclosures was earlier described as a key requirement for the Open Ethics Transparency Protocol, OETP.

Fulfillment of this requirement allows disclosure formation and validation by multiple parties and avoids harmful concentration of power. To allow efficient decentralization and access to the disclosures of autonomous systems, such as AI systems powered by trained machine learning models, the vendor (or a developer) MUST send requests to a Disclosure Identity Provider, which in turn, processes structured data of the disclosure with a cryptographic signature generator, and then stores the integrity hash with the persistent storage, for example using Federated Identity Provider. This process was described in the Open Ethics Transparency Protocol I-D document, however, the exact way how to access disclosures was not described there. The specification for the RI scheme described here closes this gap.

My recent work builds on top of our previous contribution to IETF and targets to simplify the access to AI disclosures, and more generally to disclosures of autonomous systems.

Https://Github.Com/OpenEthicsAI/OETP-RI-Scheme

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AI Disclosure

Nikita Lukianets — Sat, 13 Aug 2022 16:26:52 +0000

AI Disclosure Nikita Lukianets Sat, 08/13/2022 - 18:26

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A decentralization of SBOM (Software Bill of Materials) and data processing disclosures was earlier described as a key requirement for the Open Ethics Transparency Protocol, OETP.

My recent work builds on top of our previous contribution to IETF and targets to simplify the access to AI disclosures, and more generally to disclosures of autonomous systems.

https://github.com/OpenEthicsAI/OETP-RI-scheme

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Artificial Intelligence

A new standard for AI-based Network Applications in beyond 5G

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Addressing clinical information interoperability standards with AI standards to increase its power in its application in health.

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AI Use in the Standards Making Process

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Recall and Withdrawal of AI systems

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Cybersecurity for AI Systems

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New work ahead on NLP standards

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DIN SPEC 92005 Artificial Intelligence - Uncertainty quantification in machine learning

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Classification of AI algorithms

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Data Labeling and OETP

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AI Disclosure

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