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UKRI Trustworthy Autonomous Systems Node in Governance and Regulation

UKRI Trustworthy Autonomous Systems Node in Governance and Regulation
UKRI 治理和监管领域值得信赖的自治系统节点
批准号:
EP/V026607/1
负责人:
Subramanian Ramamoorthy
金额:
$340.44万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2020
资助国家:
英国
项目状态:
未结题
起止时间:
2020 至 --

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中文摘要
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英文摘要
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.
期刊论文(10)
专著(0)
科研奖励(0)
会议论文
Quantifying harm
量化危害
DOI: --
发表时间: 2023
期刊: Proceedings of the 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023
影响因子: --
作者: [S. Beckers, H. Chockler, J. Y. Halpern]
通讯作者: J. Y. Halpern
How do you solve a problem like Alexa?
如何解决像 Alexa 这样的问题?
DOI: 10.38023/72720413-36c5-4ee3-ba65-b7d0396fcd82
发表时间: 2023
期刊: Jusletter-IT
影响因子: --
作者: [Atabey A]
通讯作者: Atabey A
Symbol Grounding and Task Learning from Imperfect Corrections
符号基础和不完美修正的任务学习
DOI: 10.18653/v1/2021.splurobonlp-1.1
发表时间: 2021
期刊:
影响因子: --
作者: [Appelgren M]
通讯作者: Appelgren M
A Causal Analysis of Harm
危害的因果分析
DOI: 10.48550/arxiv.2210.05327
发表时间: 2022
期刊:
影响因子: --
作者: [Beckers S]
通讯作者: Beckers S
7
    Topological Methods for Learning to Steer Self-Organised Growth
    • 批准号:
      EP/X017753/1
    • 项目类别:
      Research Grant
    • 资助金额:
      $25.77万
    • 财政年份:
      2023
    • 负责人:
      Subramanian Ramamoorthy
    • 依托单位:
    海外基金