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

Chuan Chen1, Yu Feng1, Mengyi Wei1

  • 1Chair of Cartography and Visual Analytics, Technical University of Munich, Munich, Germany.

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Summary
This summary is machine-generated.

This study introduces a case-oriented ontological model (COOM) and hyper-knowledge graph system (HKGS) to address limitations in current artificial intelligence (AI) ethics research. These tools enable actionable insights and cross-domain transferability for AI ethics cases.

Keywords:
AI ethicsCase-oriented ontological modelHyper-knowledge graph systemKnowledge-based systemVisual analytics

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Area of Science:

  • Computer Science
  • Ethics
  • Information Science

Background:

  • Current artificial intelligence (AI) ethics research often lacks practical application due to overly broad guidelines or narrow domain focus.
  • Existing studies fail to explore correlations between AI ethics cases across different levels of detail, hindering knowledge transfer.
  • There is a need for a structured approach to model and analyze AI ethics cases for better understanding and application.

Purpose of the Study:

  • To develop a novel case-oriented ontological model (COOM) for structuring AI ethics cases.
  • To create a hyper-knowledge graph system (HKGS) for storing and analyzing correlations between AI ethics cases.
  • To enable advanced visual analysis of AI ethics case interdependencies and their evolution within value chains.

Main Methods:

  • Designed a case-oriented ontological model (COOM) with event, relational, and positional attributes.
  • Developed a hyper-knowledge graph system (HKGS) to store and visualize correlations between AI ethics cases.
  • Implemented HKGS using a sample AI ethics case to demonstrate graph generation and visual analysis.

Main Results:

  • The COOM provides a standardized framework for modeling diverse AI ethics cases.
  • HKGS effectively stores and visualizes complex relationships and dynamic changes among AI ethics cases.
  • Demonstrated the practical utility of HKGS for analyzing specific AI ethics scenarios.

Conclusions:

  • The proposed COOM and HKGS offer a robust solution for bridging the gap between AI ethics theory and practice.
  • HKGS facilitates a deeper understanding of AI ethics case correlations, promoting knowledge transfer across domains.
  • The system empowers various AI ethics practitioners with tools for informed decision-making and scenario exploration.