Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation

Соединяя точки в сфере заслуживающего доверия искусственного интеллекта: от принципов ИИ, этики и ключевых требований к ответственным системам ИИ и регулированию
Natalia Díaz-Rodríguez, Javier Del Ser, Francisco Herrera, Enrique Herrera‐Viedma, Mark Coeckelbergh, Marcos López de Prado
2023-06-24

AI auditingAI ethicsAI regulationResponsible AI systemsTrustworthy Artificial Intelligence
Trustworthy Artificial Intelligence (AI) is based on seven technical requirements sustained over three main pillars that should be met throughout the system’s entire life cycle: it should be (1) lawful, (2) ethical, and (3) robust, both from a technical and a social perspective. However, attaining truly trustworthy AI concerns a wider vision that comprises the trustworthiness of all processes and actors that are part of the system’s life cycle, and considers previous aspects from different lenses. A more holistic vision contemplates four essential axes: the global principles for ethical use and development of AI-based systems, a philosophical take on AI ethics, a risk-based approach to AI regulation, and the mentioned pillars and requirements. The seven requirements (human agency and oversight; robustness and safety; privacy and data governance; transparency; diversity, non-discrimination and fairness; societal and environmental wellbeing; and accountability) are analyzed from a triple perspective: What each requirement for trustworthy AI is, Why it is needed, and How each requirement can be implemented in practice. On the other hand, a practical approach to implement trustworthy AI systems allows defining the concept of responsibility of AI-based systems facing the law, through a given auditing process. Therefore, a responsible AI system is the resulting notion we introduce in this work, and a concept of utmost necessity that can be realized through auditing processes, subject to the challenges posed by the use of regulatory sandboxes. Our multidisciplinary vision of trustworthy AI culminates in a debate on the diverging views published lately about the future of AI. Our reflections in this matter conclude that regulation is a key for reaching a consensus among these views, and that trustworthy and responsible AI systems will be crucial for the present and future of our society.
1
A holistic trustworthiness framework integrates global AI principles, philosophical ethics, risk-based regulation, and the three pillars with their seven requirements.
2
The authors conclude that regulation is essential for reconciling divergent views about AI’s future and enabling trustworthy, responsible AI systems.
3
The paper introduces responsible AI systems as trustworthy systems whose legal responsibility can be addressed through auditing processes, while acknowledging challenges from regulatory sandboxes.
4
The seven requirements are examined through what they mean, why they are necessary, and how they can be implemented practically.
5
Trustworthy AI is framed around seven technical requirements supported by three pillars: lawfulness, ethics, and technical and social robustness throughout the system life cycle.

Trustworthy and responsible AI-based systems (AI systems throughout their life cycle)

the principles, ethical and legal requirements, risk-based regulation, auditing, and implementation of trustworthiness and responsibility in AI systems

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Publication Date
2023-06-24
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Authors
Natalia Díaz-Rodríguez
Javier Del Ser
Francisco Herrera
Enrique Herrera‐Viedma
Mark Coeckelbergh
Marcos López de Prado
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