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Designing for Data Autonomy Through Decentralisation

Designing for Data Autonomy Through Decentralisation
通过去中心化实现数据自治设计
批准号:
2751786
负责人:
金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Studentship
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
这项拟议的研究旨在探索数据自治和去中心化系统的交集,重点是用户如何在去中心化范例促进的在线社交媒体互动期间,保持对其个人数据及其使用的更大控制。行为工程和错误信息的传播已经成为社交媒体平台上的一个严峻挑战。为用户提供更透明、更可信的信息正在成为人工智能时代最突出的挑战之一。该项目强调以用户为中心的方法论,寻求构建一个新颖的去中心化社交媒体框架,开启一个以更分散和自主的方式管理个人数据的时代。该项目将涉及了解用户在新型社交媒体范例中开发和行使数据自主权的障碍,创建新的负责任的人工智能推荐系统,透明的数据治理模式,以及用于增强用户数据自主权的参与式设计的方法。它还将为英国及其他地区当前的行业最佳实践发展以及监管和政策发展提供投入。该项目属于EPSRC“信息和通信技术”研究领域及其主题“人工智能、数字化和数据”。该项目与牛津马丁学院伦理网络和数据架构(EWADA)项目有关。在这个研究项目中,我们有以下潜在的研究问题:当前用户如何看待分散式社交媒体中的数据自治概念,他们对最佳支持机制的需求?用户对分散式数据共享和管理结构的理解和偏好是什么,他们如何展望自己理想的数据治理和社交互动模式?在去中心化的社交媒体中增强对数据的自主性和控制力时,哪些功能或设计最能引起用户的共鸣?这些研究问题背后的驱动力是,他们迫切希望了解,在去中心化系统的背景下,普通用户是如何解释和重视数据自治的。这些基础知识将对指导增强用户控制和数据自治的框架和工具的设计至关重要。研究过程将需要深入研究用户对分散系统的价值观,同时理解他们在完全自治和易用性之间驾驭连续体的意愿。总而言之,这个项目努力从多个角度探索分散数据管理的领域:数据自治、数据隐私、问责和数据共享。这项研究是新颖的,因为以前的工作没有严格研究用户对以增强数据自主性为中心的去中心化系统的看法。最重要的是确保用户不仅可以控制他们的个人数据,而且支持这种自主性的底层系统是透明的、有弹性的和用户友好的。
英文摘要
The proposed research aims to probe the intersection of data autonomy and decentralised systems, focusing on how users can retain greater control over their personal data and its uses during their online social media interactions facilitated by decentralised paradigms. The spread of behavioural engineering and misinformation has become an acute challenge on social media platforms. Providing users with more transparent and trustworthy information is emerging as one of the most outstanding challenges in the age of AI. Emphasising a user-centric methodology, the project seeks to architect a novel decentralised social media framework, ushering in an era where personal data is managed in a more decentralised and autonomous manner. The project will involve the understanding of barriers for users to develop and exercise data autonomy in new types of social media paradigms, the creation of new responsible AI recommendation systems, transparent data governance models, and methodologies for participatory designs to enhance user data autonomy. It will also provide inputs to current industrial best practice development and regulatory and policy developments in the UK and beyond. This project falls within the EPSRC "information and communication technologies" research area and its themes of "artificial intelligence, digitisation and data". The project has connections with the Oxford Martin School Ethical Web and Data Architecture (EWADA) project.In this research project, we have the following potential research questions:How do current users perceive the concept of data autonomy within decentralised social media, and what are their needs for optimal support mechanisms?What are users' understandings and preferences concerning decentralised data sharing and management structures, and how do they envision their ideal data governance and social interaction paradigms?What features or designs resonate most with users when aiming for enhanced autonomy and control over their data within decentralised social media?The driving force behind these research questions is the urge to grasp how general users interpret and value data autonomy in the context of decentralised systems. This foundational knowledge will be critical in guiding the design of frameworks and tools that augment user control and data autonomy. The research process will necessitate delving into users' values regarding decentralised systems, while also comprehending their willingness to navigate the continuum between complete autonomy and ease-of-use.To summarise, this project endeavours to explore the realms of decentralised data management from multiple angles: data autonomy, data privacy, accountability, and data sharing. This investigation is novel as prior works have not rigorously studied user perceptions of decentralised systems centred around enhancing data autonomy. It is of paramount importance to ensure users not only have control over their personal data but also that the underlying systems supporting this autonomy are transparent, resilient, and user-friendly.
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国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
Data-driven Recommendation System Construction of an Online Medical Platform Based on the Fusion of Information
Development of a Linear Stochastic Model for Wind Field Reconstruction from Limited Measurement Data
  • 批准号:
    --
  • 项目类别:
    --
  • 资助金额:
    40万元
  • 批准年份:
    2020
  • 负责人:
    Vikrant Gupta
  • 依托单位:
基于Linked Open Data的Web服务语义互操作关键技术
  • 批准号:
    61373035
  • 项目类别:
    面上项目
  • 资助金额:
    77.0万元
  • 批准年份:
    2013
  • 负责人:
    冯志勇
  • 依托单位: