mlsanityData to 7 Oct 2026

Ethical AI Governance: Methods for Evaluating Trustworthy AI

arXiv 2409.07473 ↗First posted 28 Aug 2024cs.CY cs.AIcited by 2

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Abstract (arXiv)

Trustworthy Artificial Intelligence (TAI) integrates ethics that align with human values, looking at their influence on AI behaviour and decision-making. Primarily dependent on self-assessment, TAI evaluation aims to ensure ethical standards and safety in AI development and usage. This paper reviews the current TAI evaluation methods in the literature and offers a classification, contributing to understanding self-assessment methods in this field.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 15 of its 58 references that are in the map. First posted on arXiv 28 Aug 2024.

Builds on

15 of 58 references.

References · 15

  1. 2021Trustworthy AI: A Computational Perspectivecited by 93
  2. 2023The Foundation Model Transparency Indexcited by 63
  3. 2024Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Toolingcited by 27
  4. 2021The Role of Explainability in Assuring Safety of Machine Learning in Healthcarecited by 26
  5. 2022Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineeringcited by 20
  6. 2020Improving Fairness and Privacy in Selection Problemscited by 16
  7. 2023Investigating Practices and Opportunities for Cross-functional Collaboration around AI Fairness in Industry Practicecited by 15
  8. 2022Fairness Score and Process Standardization: Framework for Fairness Certification in Artificial Intelligence Systemscited by 14
  9. 2022A Transparency Index Framework for AI in Educationcited by 13
  10. 2020Towards a Framework for Certification of Reliable Autonomous Systemscited by 12
  11. 2022Towards Responsible AI: A Design Space Exploration of Human-Centered Artificial Intelligence User Interfaces to Investigate Fairnesscited by 9
  12. 2021Toward a Taxonomy of Trust for Probabilistic Machine Learningcited by 8
3 more
  1. 2024From COBIT to ISO 42001: Evaluating Cybersecurity Frameworks for Opportunities, Risks, and Regulatory Compliance in Commercializing Large Language Modelscited by 7
  2. 2017An End-To-End Machine Learning Pipeline That Ensures Fairness Policiescited by 3
  3. 2021Developing a novel fair-loan-predictor through a multi-sensitive debiasing pipeline: DualFaircited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 1.

2 papers.

  1. 2024A Comprehensive Survey and Classification of Evaluation Criteria for Trustworthy Artificial Intelligencecited by 3
  2. 2026Exploring the Ethical Concerns in User Reviews of Mental Health Apps using Topic Modeling and Sentiment Analysiscited by 0