Quality Models for Artificial Intelligence Systems: Characteristic-Based Approach, Development and Application

Author:

Kharchenko VyacheslavORCID,Fesenko HermanORCID,Illiashenko OlegORCID

Abstract

The factors complicating the specification of requirements for artificial intelligence systems (AIS) and their verification for the AIS creation and modernization are analyzed. The harmonization of definitions and building of a hierarchy of AIS characteristics for regulation of the development of techniques and tools for standardization, as well as evaluation and provision of requirements during the creation and implementation of AIS, is extremely important. The study aims to develop and demonstrate the use of quality models for artificial intelligence (AI), AI platform (AIP), and AIS based on the definition and ordering of characteristics. The principles of AI quality model development and its sequence are substantiated. Approaches to formulating definitions of AIS characteristics, methods of representation of dependencies, and hierarchies of characteristics are given. The definitions and harmonization options of hierarchical relations between 46 characteristics of AI and AIP are suggested. The quality models of AI, AIP, and AIS presented in analytical, tabular, and graph forms, are described. The so-called basic models with reduced sets of the most important characteristics are presented. Examples of AIS quality models for UAV video navigation systems and decision support systems for diagnosing diseases are described.

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

Reference124 articles.

1. A Systematic Review of Explainable Artificial Intelligence in Terms of Different Application Domains and Tasks

2. High-Level Expert Group on Artificial Intelligence. Ethics Guidelines for Trustworthy AI https://www.aepd.es/sites/default/files/2019-12/ai-ethics-guidelines.pdf

3. High-Level Expert Group on Artificial Intelligence. The Assessment List for Trustworthy Artificial Intelligence (ALTAI) https://airegio.ems-carsa.com/nfs/programme_5/call_3/call_preparation/ALTAI_final.pdf

4. ISO/IEC TR 24372:2021. Information Technology—Artificial Intelligence (AI)—Overview of Computational Approaches for AI Systems https://www.iso.org/standard/78508.html

5. ISO/IEC TR 24030:2021 Information Technology—Artificial Intelligence (AI)—Use Cases https://www.iso.org/standard/77610.html

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