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.
Keywords: AI ethics; Case-oriented ontological model; Hyper-knowledge graph system; Knowledge-based system; Visual analytics.
© 2024 The Authors.