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Artificial intelligence auditability and auditor readiness for auditing artificial intelligence systems
16 references·4 citing·45 second hop
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References

  • 2019Model Cards for Model Reporting2,653 cited
  • 2018Mitigating Unwanted Biases with Adversarial Learning1,387 cited
  • 2012Audit Quality: Insights from the Academic Literature788 cited
  • 2020Bridging the Gap Between Ethics and Practice747 cited
  • 2020Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency475 cited
  • 2022Thinking responsibly about responsible AI and ‘the dark side’ of AI431 cited
  • 2021Ethics Guidelines for Trustworthy AI406 cited
  • 2019The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems307 cited
  • 2020The relationship between trust in AI and trustworthy machine learning technologies296 cited
  • 2020Designing fair AI for managing employees in organizations: a review, critique, and design agenda268 cited
  • 2021Ethics-Based Auditing to Develop Trustworthy AI267 cited
  • 2022Trustworthy AI: A Computational Perspective261 cited
  • 2021Putting AI ethics to work: are the tools fit for purpose?252 cited
  • 2021Governing AI safety through independent audits249 cited
  • 2022Auditing the AI auditors: A framework for evaluating fairness and bias in high stakes AI predictive models.233 cited

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  • 2018Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society201 cited
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    • 2025Bias in AI-driven HRM systems: Investigating discrimination risks embedded in AI recruitment tools and HR analytics32 cited
    • 2024AI and ethical accounting: Navigating challenges and opportunities27 cited
    • 2025Readiness for artificial intelligence adoption by auditors in emerging countries – a PLS-SEM analysis of Moroccan firms23 cited
    • 2026A Review of TRiSM Frameworks in Artificial Intelligence Systems: Fundamentals, Taxonomy, Use Cases, Key Challenges and Future Directions20 cited