[论文解读] Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation

Natalia Díaz-Rodríguez, Javier Del Ser+4 more|arXiv (Cornell University)|May 2, 2023

Ethics and Social Impacts of AI被引用 34

一句话总结

本文提出一个将伦理原则、哲学伦理、基于风险的监管和技术要求联系起来的整体框架，以定义负责任的AI系统，并通过审计和监管沙盒来促进。

ABSTRACT

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.

研究动机与目标

• 综合一个横跨原则、伦理、监管和技术要求的可信AI的多维视角。
• 将AI原则转化为负责任的AI系统的实际要求和审计流程。
• 将欧盟AI法案的基于风险的监管作为可信AI的务实治理模型进行分析。
• 提出将监管与技术和组织实践整合的负责任AI系统作为一个结果。

提出的方法

• 分析联合国教科文组织、 Telefónica 和欧盟的伦理原则，提炼出可信AI的核心概念。
• 将可信AI分解为四个轴：伦理原则、哲学伦理、基于风险的监管和技术要求。
• 通过‘是什么、为什么、如何’的视角，审视七项技术与治理要求（人类代理与监督；鲁棒性与安全；隐私与数据治理；透明度；多样性与公正；社会与环境福祉；问责制）。
• 讨论监管沙盒和审计作为将责任落地于AI系统的机制。
• 提供以案例研究为导向的AI医疗保健视角，以说明审计与合规。
• 讨论未来方向，包括通用人工智能和动态监管。

实验结果

研究问题

• RQ1 如何在伦理原则、哲学、监管和技术要求之间实现可信AI的落地？
• RQ2 七项可信性要求的具体实现和审计方法是什么？
• RQ3 基于风险的欧盟AI法框架如何转化为对高风险AI系统的实际义务？
• RQ4 监管沙盒在实现负责任AI系统中扮演什么角色？
• RQ5 关于新兴AI系统，如GPAIS与神经科学技术，存在哪些辩论和监管空白？

主要发现

• 可信AI建立在将原则、伦理、监管和技术连接起来的四个轴上。
• 七项可信性要求可以通过它们是什么、为什么需要以及如何在实践中实现来分析。
• 监管沙盒和审计被提出作为实现负责任AI系统的实际工具。
• 欧盟AI法案采用基于风险的方法，设有四个风险等级以及对高风险系统的具体义务。
• 高风险AI类别包括执法、教育准入、招聘和关键基础设施等领域，为合格评定和治理提供信息。