These tools and metrics are designed to help AI actors develop and use trustworthy AI systems and applications that respect human rights and are fair, transparent, explainable, robust, secure and safe.
Origin
Scope
SUBMIT A TOOL
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SUBMITISO/IEC 25023 - Application of ISO 14971 to machine learning in artificial intelligence. Guide
ProceduralUploaded on Jul 3, 2024Defining quality measures for quantitatively evaluating system and software product quality in terms of characteristics and subcharacteristics defined in ISO/IEC 25010 and is intended to be used together with ISO/IEC 25010.
Objective(s)
IEEE 3301-2022 - Adoption of Moving Picture, Audio and Data Coding by Artificial Intelligence (MPAI) Technical Specification Artificial Intelligence Framework (AIF) Version 1
ProceduralUploaded on Jul 1, 2024The MPAI AI Framework (MPAI-AIF) Technical Specification specifies architecture, interfaces, protocols and Application Programming Interfaces (API) of an AI Framework (AIF), especially designed for execution of AI-based implementations, but also suitable for mixed AI and traditional data processing workflows.
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IEEE 3302-2022 - Adoption of Moving Picture, Audio and Data Coding by Artificial Intelligence (MPAI) Technical Specification Context-based Audio Enhancement (CAE) Version 1.2
ProceduralUploaded on Jul 1, 2024MPAI-CAE V1.4 is a collection of four use cases to improve the user audio experience in a variety of situations.
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IEEE 3300-2022 - Adoption of Moving Picture, Audio and Data Coding by Artificial Intelligence (MPAI) Technical Specification Multimodal Conversation Version 1.1
ProceduralUploaded on Jul 1, 2024This standard adopts MPAI Technical Specification Version 1.2 as an IEEE Standard.
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ITU-T Y.3177 - Architectural framework for artificial intelligence-based network automation for resource and fault management in future networks including IMT-2020
ProceduralUploaded on Jul 1, 2024This Recommendation specifies an architectural framework for network automation based on artificial intelligence (AI) for resource and fault management in future networks, including international mobile telecommunications-2020 (IMT-2020).
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ITU-T Y.3172 - Architectural framework for machine learning in future networks including IMT-2020
ProceduralUploaded on Jul 1, 2024Recommendation ITU-T Y.3172 specifies an architectural framework for machine learning (ML) in future networks including IMT-2020.
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ITU-T Y.3179 - Architectural framework for machine learning model serving in future networks including IMT-2020
ProceduralUploaded on Jul 1, 2024This Recommendation provides an architectural framework for machine learning (ML) models serving in future networks including IMT-2020, i.
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DIN SPEC 92001-1 - Artificial Intelligence - Life Cycle Processes and Quality Requirements - Part 1: Quality Meta Model
ProceduralUploaded on Jul 2, 2024This DIN SPEC in accordance with the PAS procedure has been drawn up by a DIN SPEC (PAS)-consortium set up on a temporary basis.
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DIN SPEC 92001-2 - Artificial Intelligence - Life Cycle Processes and Quality Requirements - Part 2: Robustness
ProceduralUploaded on Jul 2, 2024The DIN SPEC series describes a number of AI quality requirements which are structured using an AI quality meta model. The DIN SPEC series applies to all phases of the life cycle of an AI module.
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IEC 62243:2012/IEEE Std 1232-2010 - Artificial intelligence exchange and service tie to all test environments (AI-ESTATE)
ProceduralUploaded on Jul 2, 2024The purpose of AI-ESTATE is to standardize interfaces for functional elements of an intelligent diagnostic reasoner and representations of diagnostic knowledge and data for use by such diagnostic reasoners
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SAE AIR 6988 - Artificial Intelligence in Aeronautical Systems: Statement of Concerns
ProceduralUploaded on Jul 2, 2024This document reviews current aerospace software, hardware, and system development standards used in the certification/approval process of safety-critical airborne and ground-based systems, and assesses whether these standards are compatible with a typical Artificial Intelligence (AI) and Machine Learning (ML) development approach.
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ITU-R BT.2447-2/ITU-R BT.2447 - Artificial intelligence systems for programme production and exchange
ProceduralUploaded on Jul 2, 2024New broadcasting technologies driven by artificial intelligence (AI) are being introduced to the broadcasting workflow. This Report discusses current applications and efforts underway and evaluated that are relevant to broadcast programme and production pathway.
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BS PAS 1881:2022 - Assuring the operational safety of automated vehicles. Specification
ProceduralUploaded on Jul 2, 2024First full revision of PAS 1881:2020 to reflect learning from recent automated vehicle trials and input from stakeholders. It deals with how to build operational safety cases for trialling and testing of AVs so that ultimately connected and automated vehicles can be deployed safely, and the public will be confident about their safety.
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ITU-T Y.3654 - Big data driven networking - Machine learning mechanism
ProceduralUploaded on Jul 2, 2024Recommendation ITU-T Y.3654 specifies the mechanisms of machine learning in big data driven networking (bDDN).
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ITU-T Y.3531 - Cloud computing - Functional requirements for machine learning as a service
ProceduralUploaded on Jul 2, 2024This Recommendation provides system context, functional requirements and use cases for machine learning as a service (MLaaS).
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BSI Flex 1890 v4:2022-03 - Connected and automated vehicles. Vocabulary
ProceduralUploaded on Jul 2, 2024BSI Flex 1890 defines terms, abbreviations, and acronyms for the connected and automated vehicles (CAVs) sector, focused on those relating to vehicles and associated technologies.
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BS PAS 11281:2018 - Connected automotive ecosystems. Impact of security on safety. Code of practice
ProceduralUploaded on Jul 2, 2024PAS 11281 is the international standard on road vehicles that gives recommendations for managing security risks that might lead to a compromise of safety in a connected automotive ecosystem.
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VDI-EE 4030 - Consideration of human reliability in the design of autonomous systems
ProceduralUploaded on Jul 2, 2024The scope of the expert recommendation is the design and layout of autonomous systems from the perspective of human reliability.
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ETSI EG 203341 V 1.1.1 - Core Network and Interoperability Testing (INT) - Approaches for Testing Adaptive Networks
ProceduralUploaded on Jul 2, 2024Methodology extending current experiences on the characteristics of 'adaptive networks' such as virtualization, self-organization, self-configuration, self-optimization, self-healing and self-learning offer huge advantages in future networks.
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ITU-T L.1305 - Data centre infrastructure management system based on big data and artificial intelligence technology
ProceduralUploaded on Jul 2, 2024This Recommendation describes specifications of a data centre infrastructure management (DCIM) system based on big data and artificial intelligence (AI) technology.
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