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A hyper-knowledge graph system for research on AI ethics cases
Journal article   Open access   Peer reviewed

A hyper-knowledge graph system for research on AI ethics cases

Chuan Chen, Yu Feng, Mengyi Wei, Zihan Liu, Peng Luo, Liqiu Meng and Shengkai Wang
Heliyon, Vol.10(7), p.e29048
04/15/2024
DOI: 10.1016/j.heliyon.2024.e29048
PMID: 38601681
url
https://doi.org/10.1016/j.heliyon.2024.e29048View
Published (Version of record) Open Access

Abstract

Current studies on the artificial intelligence (AI) ethics focus either on very broad guidelines or on a very special domain. Therefore, the research outcome can hardly be converted into actionable measures or transferred to other domains. Potential correlations between various cases of AI ethics at different granularity levels are unexplored. To overcome these deficiencies, the authors designed a case -oriented ontological model (COOM) and a hyper -knowledge graph system (HKGS) for the research of collected AI ethics cases. COOM describes criteria for modelling cases by attributes from three perspectives: event attributes, relational attributes, and positional attributes on the value chain. Based on it, HKGS stores the correlation between cases as knowledge and allows advanced visual analysis. The correlations between cases and their dynamic changes on value chain can be observed and explored. In HKGS ' s implementation part, one of the collected ethics cases is used as an example to demonstrate how to generate a hyper -knowledge graph and to visually analyze it. The authors also anticipated how different practitioners of AI ethics, can achieve the desired outputs from HKGS in their diverse scenarios.
 Multidisciplinary Sciences Science & Technology Science & Technology - Other Topics

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