mlsanityData to 7 Oct 2026

A Comprehensive Survey and Classification of Evaluation Criteria for Trustworthy Artificial Intelligence

arXiv 2410.17281 ↗First posted 10 Oct 2024cs.CY cs.AI cs.HCcited by 3

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Abstract (arXiv)

This paper presents a systematic review of the literature on evaluation criteria for Trustworthy Artificial Intelligence (TAI), with a focus on the seven EU principles of TAI. This systematic literature review identifies and analyses current evaluation criteria, maps them to the EU TAI principles and proposes a new classification system for each principle. The findings reveal both a need for and significant barriers to standardising criteria for TAI evaluation. The proposed classification contributes to the development, selection and standardization of evaluation criteria for TAI governance.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 35 of its 96 references that are in the map. First posted on arXiv 10 Oct 2024.

Builds on

35 of 96 references.

References · 35

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23 more
  1. 2020Fairness and Robustness in Invariant Learning: A Case Study in Toxicity Classificationcited by 32
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Bridging the Gap in XAI-Why Reliable Metrics Matter for Explainability and Compliancecited by 6
  2. 2026AI Transparency: Governance Compliance or Stakeholder Requirements?cited by 0
  3. 2026Can Valence Reflect Morality in Natural Language? A Preliminary Annotation Studycited by 0