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A comprehensive survey and classification of evaluation criteria for trustworthy artificial intelligence
16 references·1 citing·44 second hop
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References

  • 1981Evaluating Structural Equation Models with Unobservable Variables and Measurement Error108,884 cited
  • 2021A Survey on Bias and Fairness in Machine Learning5,798 cited
  • 2018AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations4,732 cited
  • 2013A systematic review of systematic review process research in software engineering1,094 cited
  • 2022Trustworthy Artificial Intelligence: A Review707 cited
  • 2022A Review on Fairness in Machine Learning596 cited
  • 2021Artificial Intelligence Regulation: a framework for governance395 cited
  • 2023Bias and Unfairness in Machine Learning Models: A Systematic Review on Datasets, Tools, Fairness Metrics, and Identification and Mitigation Methods315 cited
  • 2022Trustworthy AI: A Computational Perspective261 cited
  • 2022Auditing the AI auditors: A framework for evaluating fairness and bias in high stakes AI predictive models.233 cited
  • 2021Fairness in credit scoring: Assessment, implementation and profit implications196 cited
  • 2022Artificial Intelligence Governance For Businesses168 cited
  • 2022Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learning133 cited
  • 2020Algorithmic Fairness in Mortgage Lending: from Absolute Conditions to Relational Trade-offs

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106 cited
  • 2022Training data debugging for the fairness of machine learning software60 cited
  • 2022Bias, Fairness and Accountability with Artificial Intelligence and Machine Learning Algorithms57 cited
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    • 2025Trust and transparency in AI: industry voices on data, ethics, and compliance12 cited