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Automatic transparency evaluation for open knowledge extraction systems.

Maryam Basereh1, Annalina Caputo2, Rob Brennan3

  • 1School of Computing, Dublin City University, Dublin, Ireland. maryam.basereh@adaptcentre.ie.

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|August 31, 2023
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Summary
This summary is machine-generated.

This study introduces Cyrus, a transparency evaluation framework for Open Knowledge Extraction (OKE) systems. Cyrus quantifies OKE system transparency, revealing differences in linked data quality and aiding trustworthy AI development.

Keywords:
Automatic transparency evaluationFAIRness assessmentOpen knowledge extractionQuality evaluationTransparency framework

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Area of Science:

  • Artificial Intelligence
  • Data Science
  • Information Science

Background:

  • Open Knowledge Extraction (OKE) systems are crucial for knowledge services but often lack transparency, making their processes and outcomes difficult to understand.
  • The European Union highlights trustworthy Artificial Intelligence (AI) and the need for transparency in AI systems.
  • Existing transparency models and linked data quality assessments are fragmented, necessitating a unified framework for OKE systems.

Purpose of the Study:

  • To propose Cyrus, a novel transparency evaluation framework specifically designed for Open Knowledge Extraction (OKE) systems.
  • To provide a comprehensive view of transparency dimensions by integrating perspectives from FAccT, FAIR, and linked data quality research.
  • To enable automated transparency assessment for OKE systems, facilitating comparison and improvement.

Main Methods:

  • Developed the Cyrus framework based on state-of-the-art transparency models and linked data quality dimensions.
  • Evaluated the transparency of three linked datasets generated by state-of-the-art OKE systems using the Cyrus framework.
  • Employed automated evaluation combining FAIRness assessment tools and the Luzzu linked data quality framework to assess six Cyrus transparency dimensions.

Main Results:

  • The Cyrus framework successfully evaluated the transparency of three OKE systems across six key dimensions: provenance, interpretability, understandability, licensing, availability, and interlinking.
  • Covid-on-the-Web demonstrated the highest mean transparency among the evaluated OKE systems.
  • Automated quantification of transparency differences across state-of-the-art OKE systems was achieved.

Conclusions:

  • Cyrus offers the first comprehensive transparency framework for OKE systems, merging ethical, trustworthy AI, and data quality viewpoints.
  • Automated transparency evaluation for OKE systems is feasible by integrating existing FAIRness and data quality assessment tools.
  • The study demonstrates that OKE system transparency varies and can be automatically quantified, with implications for trustworthy AI, compliance, and system design.