Catalogue of Tools & Metrics for Trustworthy AI

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.

DIN SPEC 92001-1 - Artificial Intelligence - Life Cycle Processes and Quality Requirements - Part 1: Quality Meta Model



This DIN SPEC in accordance with the PAS procedure has been drawn up by a DIN SPEC (PAS)-consortium set up on a temporary basis. This DIN SPEC (PAS) has been developed and approved by the authors named in the foreword. This DIN SPEC is divided into two parts. Part 1, DIN SPEC 92001-1, provides a general quality meta model for artificial intelligence (AI) that will primarily describe the most important aspects of AI quality. The AI quality metal model features quality characteristics, most notably performance & functionality, robustness and comprehensibility. This document also deals with the AI module as part of a software system and assesses the risks associated with this module; it also describes a suitable software life cycle approach. The present AI life cycle approach leans heavily on the International Standard on system and software development ISO/IEC/IEEE 12207:2017. The aim of this series of DIN SPECs is to enable the safe, transparent development and use of AI modules. To achieve this, the DIN SPEC describes a number of quality requirements that are structured using an AI quality meta model. The DIN SPEC series applies to all phases in the life cycle of an AI module (i.e. its conceptualization, development, use, operation and termination) and takes into account a number of different life cycle processes. AI technologies are used for a diverse range of applications, which is why this DIN SPEC series is aimed at companies across all sectors. © 2023 Beuth Verlag GmbH

The information about this standard has been compiled by the AI Standards Hub, an initiative dedicated to knowledge sharing, capacity building, research, and international collaboration in the field of AI standards. You can find more information and interactive community features related to this standard by visiting the Hub’s AI standards database here. To access the standard directly, please visit the developing organisation’s website.

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  • robustness
  • Accuracy and performance
  • safety

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