Abstract
Many 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-hbox {MC}^2) 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-hbox {MC}^2 Grid 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.
Highlights
With the artificial intelligence (AI) Methods, Capabilities and Criticality (AI-MC2 ) Grid, we have introduced a practical classification scheme for AI applications that consists of three dimensions: AI methods, AI capabilities and the criticality of the AI application
Each discussed AI application can be placed in these three dimensions and compared along this dimensions
Complex AI solutions can be analyzed by placing its individual components within the AI-MC2 Grid
Summary
The European Union has initialized a regulation proposal on harmonized rules for artificial intelligence (AI) [2, 3]. KI - Künstliche Intelligenz trend to use and develop AI for commercial products has accelerated considerably since 2010 [5, 6] This is primarily driven by both the huge increases in the volume of available digital data, as well as the increased in processing power that can run improved methods such as neural networks faster and more cost-effectively [7, 8]. To facilitate the use of such applications, commercial services and products are typically offered from public or private cloud environments This allows users to start immediately with the adaptation of the service or product to their own needs without spending time and effort on building hardware and software. These markets of AI products and services can be roughly divided into the main areas of business intelligence [12] and decision making [13], AI-based customer interaction [14], AI-based services [15] and AI development environments and tools
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