{"@context":"https://schema.org","@type":"ScholarlyArticle","headline":"The AI Methods, Capabilities and Criticality Grid","author":[{"@type":"Person","name":"Thomas Schmid"},{"@type":"Person","name":"W. Hildesheim"},{"@type":"Person","name":"Taras Holoyad"},{"@type":"Person","name":"Kinga Schumacher"}],"datePublished":"2021","isPartOf":{"@type":"Periodical","name":"KI - Künstliche Intelligenz"},"identifier":"https://doi.org/https://doi.org/10.1007/s13218-021-00736-4","description":"AbstractMany artificial intelligence (AI) technologies developed over the past decades have reached market maturity and are now being commercially distributed in digital products and services. Therefore, national and international AI standards are currently being developed in order to achieve technical interoperability as well as reliability and transparency. To this end, we propose to classify AI applications in terms of the algorithmic methods used, the capabilities to be achieved and the level of criticality. The resulting three-dimensional classification scheme, termed the AI Methods, Capabilities and Criticality (AI-

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MC2) Grid, combines current recommendations of the EU Commission with an ethical dimension proposed by the Data Ethics Commission of the German Federal Government (Datenethikkommission der Bundesregierung: Gutachten. Berlin, 2019). As a whole, the AI-

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MC2Grid allows not only to gain an overview of the implications of a given AI application as well as to compare efficiently different AI applications within a given market or implemented by different AI technologies. It is designed as a core tool to define and manage norms, standards and compliance of AI applications, but helps to manage AI solutions in general as well.","url":"https://innscience.ca/en/community/literature/94daffe6-e328-44d4-a6fe-0d09e45dc207"}