Literature DB >> 35808361

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

Vyacheslav Kharchenko1, Herman Fesenko1, Oleg Illiashenko1.   

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.

Entities:  

Keywords:  artificial intelligence; characteristic; quality; quality model

Mesh:

Year:  2022        PMID: 35808361      PMCID: PMC9269736          DOI: 10.3390/s22134865

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.847


  9 in total

1.  Engineering Bias in AI.

Authors:  Cynthia Weber
Journal:  IEEE Pulse       Date:  2019 Jan-Feb       Impact factor: 0.924

Review 2.  The role of explainability in creating trustworthy artificial intelligence for health care: A comprehensive survey of the terminology, design choices, and evaluation strategies.

Authors:  Aniek F Markus; Jan A Kors; Peter R Rijnbeek
Journal:  J Biomed Inform       Date:  2020-12-10       Impact factor: 6.317

3.  A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI.

Authors:  Erico Tjoa; Cuntai Guan
Journal:  IEEE Trans Neural Netw Learn Syst       Date:  2021-10-27       Impact factor: 10.451

4.  Ethical funding for trustworthy AI: proposals to address the responsibilities of funders to ensure that projects adhere to trustworthy AI practice.

Authors:  Allison Gardner; Adam Leon Smith; Adam Steventon; Ellen Coughlan; Marie Oldfield
Journal:  AI Ethics       Date:  2021-06-13

Review 5.  Explainable AI: A Review of Machine Learning Interpretability Methods.

Authors:  Pantelis Linardatos; Vasilis Papastefanopoulos; Sotiris Kotsiantis
Journal:  Entropy (Basel)       Date:  2020-12-25       Impact factor: 2.524

6.  Measuring the Quality of Explanations: The System Causability Scale (SCS): Comparing Human and Machine Explanations.

Authors:  Andreas Holzinger; André Carrington; Heimo Müller
Journal:  Kunstliche Intell (Oldenbourg)       Date:  2020-01-21

Review 7.  Causability and explainability of artificial intelligence in medicine.

Authors:  Andreas Holzinger; Georg Langs; Helmut Denk; Kurt Zatloukal; Heimo Müller
Journal:  Wiley Interdiscip Rev Data Min Knowl Discov       Date:  2019-04-02

8.  In AI We Trust: Ethics, Artificial Intelligence, and Reliability.

Authors:  Mark Ryan
Journal:  Sci Eng Ethics       Date:  2020-06-10       Impact factor: 3.525

  9 in total

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