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.

翻译：可信人工智能建立在三大支柱所支撑的七项技术需求之上，这些需求应在系统整个生命周期内得到满足：即系统应（1）合法、（2）合乎伦理、（3）在技术与社会层面均具鲁棒性。然而，实现真正可信的人工智能涉及更广阔的视野，要求系统生命周期内所有流程和参与者均具备可信性，并从不同维度审视前述方面。更具整体性的视角包含四个基本轴心：基于AI系统的伦理使用与开发的全球原则、AI伦理的哲学视角、基于风险的AI监管方法，以及前述支柱与需求。七项需求（人类能动性与监督；鲁棒性与安全性；隐私与数据治理；透明度；多样性、非歧视与公平；社会与环境福祉；问责制）从三重角度进行分析：每一项可信人工智能需求的内涵、必要性及实施方法。另一方面，通过既定审计流程，采用实践方法部署可信人工智能系统，能够明确AI系统在法律面前的负责任概念。因此，负责任AI系统是本文引入的概念，也是可通过审计流程实现的迫切必要概念，但需应对监管沙盒使用带来的挑战。我们针对可信人工智能的多学科视角最终聚焦于近期关于AI未来走向的分歧观点辩论。对此的思考结论是：监管是达成观点共识的关键，而可信与负责任的AI系统将对当前及未来社会具有决定性意义。