UKRI Trustworthy Autonomous Systems Node in Governance and Regulation

我们怎么能相信基于计算机的自主系统？自治的意思是“独立，有能力自己做决定”。该提案通过构建以下内容来解决信任自治系统（AS）的问题：监管结构和实践的经验、原因、责任和责任的概念，以及在现代开发实践中创建可信度证据的工具。现代开发实践包括持续集成和持续交付。这些实践允许持续收集操作经验，通过使用模拟器将其放大，并将该经验折叠到开发决策中。这与预期监管和增量信任建立的概念相结合，形成了自主系统发展新实践的基础，其中监管、系统和可靠行为的证据共同渐进地发展，以支持我们对系统的信任。该提案是与来自爱丁堡，赫瑞瓦特，格拉斯哥，KCL，诺丁汉和苏塞克斯的多学科团队合作的，汇集了计算机科学和人工智能专家，法律学者，人工智能伦理学家，以及科学技术研究和设计人种学方面的专家。总之，我们提出了一种新的软件工程和治理方法，包括：1)有助于弥合法律和道德原则（包括围绕隐私、公平、问责制和透明度的新问题）与自主系统设计过程之间差距的新框架，该过程需要由新兴技术（包括，例如机器学习在循环决策系统）驱动的快速迭代；2)监管者生态系统的新工具；开发人员和受信任的第三方不仅要解决功能或正确性（许多其他节点的焦点），还要解决系统如何失败的问题，以及如何管理与此相关的证据以促进更好的治理。3)根据我们的合作伙伴的经验，通过监管流程采用AS的全周期案例研究的证据基础，以促进有关反思性监管实践的政策讨论。

How can we trust autonomous computer-based systems? Autonomous means "independent and having the power to make your own decisions". This proposal tackles the issue of trusting autonomous systems (AS) by building: experience of regulatory structure and practice, notions of cause, responsibility and liability, and tools to create evidence of trustworthiness into modern development practice. Modern development practice includes continuous integration and continuous delivery. These practices allow continuous gathering of operational experience, its amplification through the use of simulators, and the folding of that experience into development decisions. This, combined with notions of anticipatory regulation and incremental trust building form the basis for new practice in the development of autonomous systems where regulation, systems, and evidence of dependable behaviour co-evolve incrementally to support our trust in systems.This proposal is in consortium with a multi-disciplinary team from Edinburgh, Heriot-Watt, Glasgow, KCL, Nottingham and Sussex, bringing together computer science and AI specialists, legal scholars, AI ethicists, as well as experts in science and technology studies and design ethnography. Together, we present a novel software engineering and governance methodology that includes: 1) New frameworks that help bridge gaps between legal and ethical principles (including emerging questions around privacy, fairness, accountability and transparency) and an autonomous systems design process that entails rapid iterations driven by emerging technologies (including, e.g. machine learning in-the-loop decision making systems) 2) New tools for an ecosystem of regulators, developers and trusted third parties to address not only functionality or correctness (the focus of many other Nodes) but also questions of how systems fail, and how one can manage evidence associated with this to facilitate better governance. 3) Evidence base from full-cycle case studies of taking AS through regulatory processes, as experienced by our partners, to facilitate policy discussion regarding reflexive regulation practices.