课题基金 / 基金详情

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

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:
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
项目状态:
未结题
起止时间:
2022 至 --

项目摘要

项目成果

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中文摘要
翻译
社会在自主系统的开发和实施方面取得了巨大的增长,这可以为公民、社区和企业带来巨大的好处。改善社会福祉的潜力是巨大的。然而,这种积极的潜力被类似的社会危害的潜力所平衡,这些或有影响包括自治系统的环境足迹、一些社会经济群体的系统性劣势以及根深蒂固的数字鸿沟。因此,在推出自主系统时,必须牢记对社会的责任。这必须包括与社会和受影响的人进行对话,努力在挑战发生之前预测挑战,并对其作出反应。其中一个预期的挑战是变化对自主系统的影响。自主系统并不是被设计成在完全停滞的条件下部署的,因为它们不太可能在现实环境中遇到这样的条件。它们通常是为不断变化的环境而设计的,比如公共道路,也可能被设计成随着时间的推移而改变自己,例如通过学习能力。不仅如此,已部署系统及其运行条件的这些变化也可能是在不断变化的社会变化的背景下发生的(例如,其他技术、‘黑天鹅’事件,或仅仅是社区的日常运作)。这种变化对系统本身、对系统运行的环境以及对人类的影响,必须作为负责任的创新方法的一部分来考虑。Rails项目汇集了来自伦敦大学学院、约克大学、利兹大学和牛津大学的来自多个学科的团队,目的是应对与自治系统的长期运营相关的挑战以及变化对这些系统的影响。特别是,我们将探讨责任的概念如何受到(I)开放式动态环境--随时间变化的情况--以及(Ii)终身学习系统--即旨在使自己适应其环境并随着时间‘学习’的系统的影响。Rails项目将专注于这种独立的长期自治系统在不同的应用中。这些将包括(一)自动车辆和(二)自动机器人系统,如无人驾驶飞行器(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任、可问责和值得信赖的方式设计、开发和操作这些系统。Rails项目的总体目标是将负责任的开发原则与治理机制和技术理解结合在一起,以创建对自治系统如何适应变化、如何以负责任和可信的方式部署它们以及如何通过治理来框架此类部署以确保责任和灵活性的新理解。
英文摘要
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.
期刊论文(10)
专著(0)
科研奖励(0)
会议论文
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
期刊:
影响因子: --
作者: [Howard R]
通讯作者: Howard R
DOI: 10.48550/arxiv.2302.00064
发表时间: 2023-01
期刊: ArXiv
影响因子: --
作者: [Rhys Howard;L. Kunze]
通讯作者: Rhys Howard;L. Kunze
DOI: 10.1109/icra48891.2023.10161132
发表时间: 2023-02
期刊: 2023 IEEE International Conference on Robotics and Automation (ICRA)
影响因子: --
作者: [Pawit Kochakarn;D. Martini;Daniel Omeiza;L. Kunze]
通讯作者: Pawit Kochakarn;D. Martini;Daniel Omeiza;L. Kunze
DOI: 10.48550/arxiv.2309.09844
发表时间: 2023-09
期刊: ArXiv
影响因子: --
作者: [George Drayson;Efimia Panagiotaki;Daniel Omeiza;Lars Kunze]
通讯作者: George Drayson;Efimia Panagiotaki;Daniel Omeiza;Lars Kunze
共 6 条
    国内基金
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      省市级项目
    • 资助金额:
      --
    • 批准年份:
      2026
    • 负责人:
      刘登志
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      省市级项目
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      --
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      2026
    • 负责人:
      许蕴彰
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    • 批准号:
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      省市级项目
    • 资助金额:
      --
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      2026
    • 负责人:
      窦健泰
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    AI赋能中国传统壁画大模型开发与数字再生展示
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      省市级项目
    • 资助金额:
      --
    • 批准年份:
      2026
    • 负责人:
      朱亮亮
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