UKRI Trustworthy Autonomous Systems Node in Governance and Regulation

我们如何信任基于计算机的自主系统？自主意味着“独立，有权做出自己的决定”。该建议通过以下方式解决信任自治系统(AS)的问题：监管结构和实践的经验、原因、责任和责任的概念，以及在现代发展实践中创建可信性证据的工具。现代开发实践包括持续集成和持续交付。这些做法允许持续收集业务经验，通过使用模拟器扩大这些经验，并将这些经验纳入发展决策。这一点，再加上预见性监管和逐步建立信任的概念，构成了自主系统开发中新实践的基础，在这些系统中，监管、系统和可靠行为的证据共同进化，以支持我们对系统的信任。这项提议是与来自爱丁堡、赫里奥特-瓦特、格拉斯哥、KCL、诺丁汉和苏塞克斯的多学科团队联合提出的，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。我们共同提出了一种新颖的软件工程和治理方法，其中包括：1)帮助弥合法律和道德原则(包括有关隐私、公平、问责和透明度的新问题)和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驱动的快速迭代(例如，包括机器学习环路决策系统)2)用于监管机构、开发人员和可信第三方生态系统的新工具，以不仅解决功能或正确性(许多其他节点的焦点)，而且解决系统如何故障的问题，以及如何管理与此相关的证据以促进更好的治理。3)从我们的合作伙伴所经历的通过监管过程采取AS的全周期案例研究的证据基础，以促进关于反身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.