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Trustworthy Artificial Intelligence: Nature, Requirements, Regulation, and Emerging Discussions

In: Transactions of ADIA Lab Interdisciplinary Advances in Data and Computational Science

Author

Listed:
  • Francisco Herrera
  • Andres Herrera
  • Javier Del Ser
  • Enrique Herrera-Viedma
  • Marcos López de Prado

Abstract

Trustworthy artificial intelligence (TAI) is a framework and critical goal for developing and practically deploying reliable and ethical AI systems, properties essential for user confidence, societal acceptance, and responsible use. In this chapter, we explore TAI from three perspectives: (1) the importance of TAI and why it is essential; (2) a brief analysis of TAI requirements and characteristics as proposed by the high-level expert group on artificial intelligence commissioned by the European Commission and the American National Institute of Standards and Technology, preceded by a short discussion on trust in AI; and (3) an analysis of TAI’s role within emerging discussions on AI regulation and governance. We also review contributions from the literature, offering reflections that complement this chapter’s holistic vision of TAI, from theory to practice. TAI is expected to play a crucial role in the development of responsible AI systems, ensuring auditability and accountability throughout the design, development, and utilization phases, thereby creating ethical, reliable, and safe AI systems from a technological perspective.

Suggested Citation

  • Francisco Herrera & Andres Herrera & Javier Del Ser & Enrique Herrera-Viedma & Marcos López de Prado, 2025. "Trustworthy Artificial Intelligence: Nature, Requirements, Regulation, and Emerging Discussions," World Scientific Book Chapters, in: Horst Simon (ed.), Transactions of ADIA Lab Interdisciplinary Advances in Data and Computational Science, chapter 12, pages 317-342, World Scientific Publishing Co. Pte. Ltd..
    • Handle: RePEc:wsi:wschap:9789819813049_0012
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    Keywords

    Computational Science; Data Science; AI Applications; Climate Science; Medical Imaging; Sustainability; Interdisciplinary Research; Data Science; Mathematical and Quantitative Finance;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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