Responsible AI for Long-term Trustworthy Autonomous Systems (RAILS): Integrating Responsible AI and Socio-legal Governance

用于长期可信自治系统（RAILS）的负责任的人工智能：将负责任的人工智能与社会法律治理相结合

• 批准号: EP/W011344/1
• 负责人: Lars Kunze
• 金额: $ 90.48万
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FW011344%2F1
• 关键词: Responsible; AI; Long; term; Trustworthy

Society is seeing enormous growth in the development and implementation of autonomous systems, which can offer significant benefits to citizens, communities, and businesses. The potential for improvements in societal wellbeing is substantial. However, this positive potential is balanced by a similar potential for societal harm through contingent effects such as the environmental footprint of autonomous systems, systemic disadvantage for some socio-economic groups, and entrenchment of digital divides. The rollout of autonomous systems must therefore be addressed with responsibilities to society in mind. This must include engaging in dialogue with society and with those affected, trying to anticipate challenges before they occur, and responding to them. One such anticipated challenge is the effect of change on autonomous systems. Autonomous systems are not designed to be deployed in conditions of perfect stasis, as they are unlikely to encounter such conditions in real-world environments. They are frequently designed for changing environments, like public roads, and may also be designed to change themselves over time, for instance by means of learning capabilities. Not only that, but these changes in deployed systems and in their operating conditions are also likely to take place against a shifting contextual background of societal alteration (e.g. other technologies, 'black swan' events, or simply the day-to-day operation of communities). The effects of such change, on the systems themselves, on the environments within which they are operating, and on the humans with which they engage, must be considered as part of a responsible innovation approach. The RAILS project brings together a team from UCL and the Universities of York, Leeds and Oxford, from multiple disciplines, with the aim of engaging with the challenges associated with the long-term operation of autonomous systems and the effects of change on these systems. In particular, we will explore how the notion of responsibility is affected by (i) open-ended dynamic environments - situations that change over time, and(ii) lifelong-learning systems - i.e. systems that are designed to adapt themselves to their circumstances and 'learn' over time. The RAILS project will focus on such independent long-term autonomous systems in different applications. These will include (i) autonomous vehicles and (ii) autonomous robot systems such as unmanned aerial vehicles (drones). RAILS will look at social and legal contexts, as well as technical requirements, in order to assess whether and how these systems can be designed, developed, and operated in a way that they are responsible, accountable, and trustworthy. The overall aim of the RAILS project is to bring together responsible development principles with governance mechanisms and technical understanding to create new understandings of how autonomous systems can adapt to change, how they can be deployed in a responsible and trustworthy way, and how such deployment can be framed by governance to ensure accountability and flexibility.

社会正在看到自治系统的发展和实施巨大的增长，这可以为公民，社区和企业带来重大利益。改善社会健康的潜力是巨大的。但是，这种积极的潜力通过偶然的影响（例如自主系统的环境足迹，对于某些社会经济群体的全身劣势）以及数字分歧的根深蒂固的环境劣势来平衡社会伤害的潜力。因此，必须考虑对社会的责任来解决自主系统的推出。这必须包括与社会和受影响的人进行对话，试图在挑战发生之前预测挑战并回应它们。一个预期的挑战是变革对自主系统的影响。自主系统的设计并非被设计为在完美的停滞条件下部署，因为它们不太可能在现实世界环境中遇到这种情况。它们通常是为不断变化的环境（例如公共道路）而设计的，也可以设计为随着时间的流逝而改变自己，例如通过学习能力。不仅如此，而且在部署系统及其操作条件下的这些变化也可能发生在不断变化的社会改变背景下（例如其他技术，“黑天鹅”事件，或仅仅是社区的日常运营）。这种变化对系统本身，对其运营环境以及与之参与的人类的影响的影响必须被视为负责任的创新方法的一部分。 Rails项目汇集了来自UCL和约克，利兹和牛津大学的一个团队，来自多个学科，目的是应对与自主系统的长期运作相关的挑战以及变革对这些系统的影响。特别是，我们将探讨责任概念如何受（i）开放式动态环境的影响 - 随着时间的流逝而改变的情况以及（ii）终身学习系统 - 即旨在适应自己的环境并随着时间的流逝而“学习”的系统。 Rails项目将重点关注不同应用中这种独立的长期自治系统。这些将包括（i）自动驾驶汽车和（ii）自动驾驶机器人系统，例如无人机（无人机）。 Rails将研究社会和法律环境以及技术要求，以评估是否以及如何以负责，负责和值得信赖的方式设计，开发和运行这些系统。铁路项目的总体目的是将负责任的发展原则与治理机制和技术理解结合在一起，以对自治系统如何适应变化，如何以负责任和值得信赖的方式部署它们以及如何通过治理来确保责任感和灵活性来构成此类部署。

项目成果

Evaluating Temporal Observation-Based Causal Discovery Techniques Applied to Road Driver Behaviour

• DOI: 10.48550/arxiv.2302.00064
• 发表时间: 2023-01
• 期刊: ArXiv
• 作者: Rhys Howard;L. Kunze

Simulation-Based Counterfactual Causal Discovery on Real World Driver Behaviour

基于模拟的现实世界驾驶员行为的反事实因果发现

• DOI: 10.1109/iv55152.2023.10186705
• 发表时间: 2023
• 作者: Howard R

Explainable Action Prediction through Self-Supervision on Scene Graphs

• DOI: 10.1109/icra48891.2023.10161132
• 发表时间: 2023-02
• 期刊: 2023 IEEE International Conference on Robotics and Automation (ICRA)
• 作者: Pawit Kochakarn;D. Martini;Daniel Omeiza;L. Kunze

CC-SGG: Corner Case Scenario Generation using Learned Scene Graphs

• DOI: 10.48550/arxiv.2309.09844
• 发表时间: 2023-09
• 期刊: ArXiv
• 作者: George Drayson;Efimia Panagiotaki;Daniel Omeiza;Lars Kunze

The SAGE Handbook of Digital Society

数字社会 SAGE 手册

• DOI: 10.4135/9781529783193
• 发表时间: 2023
• 作者: Housley W