ReEnTrust: Rebuilding and Enhancing Trust in Algorithms

随着网络平台上的互动成为人们日常生活的重要组成部分，数据驱动的人工智能算法开始对社会产生巨大影响，我们正经历着用户对这些算法在网络上如何使用的巨大紧张。这些紧张关系导致了信任的崩溃：用户不知道什么时候该信任算法过程的结果，因此也不知道该信任使用它们的平台。信任是数字经济的关键组成部分，算法决策影响着公民的日常生活，因此这是一个需要解决的重大问题。re委托为平台重新获得用户信任探索了新的技术机会，并旨在确定如何以用户驱动和负责任的方式实现这一目标。专注于人工智能算法和公众使用的大规模平台，我们的研究问题包括：一旦失去信任，用户对算法系统重建信任的期望和要求是什么？是否有可能通过在推荐、预测和信息过滤算法中嵌入价值，并允许所有利益相关者之间就算法设计进行富有成效的辩论，创造出重建信任的技术解决方案？通过技术解决方案可以在多大程度上重新获得用户信任，哪些进一步的信任重建机制可能是必要和适当的，包括政策、法规和教育？该项目将开发一个实验性在线工具，允许用户评估和批评在线平台使用的算法，并与所有相关利益相关者进行对话和集体反思，以便共同从导致信任丧失的算法行为中恢复过来。为此，我们将开发新颖的、先进的人工智能驱动的调解支持技术，允许各方解释他们的观点，并提出可能的妥协解决方案。在开发这一在线工具的过程中，与用户、利益相关者和平台服务提供商的广泛接触将使人们更好地理解是什么使人工智能算法值得信赖。我们还将为技术解决方案制定政策建议和要求，以及在算法中介系统的开发中纳入信任关系的评估标准，以及为在线平台推导“信任指数”的方法，使用户能够轻松评估平台的可信度。该项目由牛津大学与爱丁堡大学和诺丁汉大学合作领导。爱丁堡大学开发了新的计算技术来评估和批评算法中嵌入的价值观，并开发了一个原型人工智能支持的平台，使用户能够就算法失败交换意见，并就如何“修复”相关算法达成一致，以重建信任。牛津大学和诺丁汉大学的团队开发了支持以用户为中心和负责任的开发这些工具的方法。这包括研究网络平台中信任崩溃和重建的过程，并开发一种负责任的研究和创新方法来理解实践中的信任和信任重建。经过精心挑选的一组工业和其他非学术合作伙伴确保re委托工作以现实世界的例子和经验为基础，并确保所有利益相关者群体的平衡、公平代表。ReEnTrust将通过开发首个人工智能支持的调解和冲突解决技术，以及全面的以用户为中心的设计和负责任的研究与创新框架，推动算法在社会中使用的共同责任方法，从而促进繁荣的数字经济，从而在算法驱动的在线平台的信任重建技术方面推进最先进的技术。

As interaction on online Web-based platforms is becoming an essential part of people's everyday lives and data-driven AI algorithms are starting to exert a massive influence on society, we are experiencing significant tensions in user perspectives regarding how these algorithms are used on the Web. These tensions result in a breakdown of trust: users do not know when to trust the outcomes of algorithmic processes and, consequently, the platforms that use them. As trust is a key component of the Digital Economy where algorithmic decisions affect citizens' everyday lives, this is a significant issue that requires addressing. ReEnTrust explores new technological opportunities for platforms to regain user trust and aims to identify how this may be achieved in ways that are user-driven and responsible. Focusing on AI algorithms and large scale platforms used by the general public, our research questions include: What are user expectations and requirements regarding the rebuilding of trust in algorithmic systems, once that trust has been lost? Is it possible to create technological solutions that rebuild trust by embedding values in recommendation, prediction, and information filtering algorithms and allowing for a productive debate on algorithm design between all stakeholders? To what extent can user trust be regained through technological solutions and what further trust rebuilding mechanisms might be necessary and appropriate, including policy, regulation, and education? The project will develop an experimental online tool that allows users to evaluate and critique algorithms used by online platforms, and to engage in dialogue and collective reflection with all relevant stakeholders in order to jointly recover from algorithmic behaviour that has caused loss of trust. For this purpose, we will develop novel, advanced AI-driven mediation support techniques that allow all parties to explain their views, and suggest possible compromise solutions. Extensive engagement with users, stakeholders, and platform service providers in the process of developing this online tool will result in an improved understanding of what makes AI algorithms trustable. We will also develop policy recommendations and requirements for technological solutions plus assessment criteria for the inclusion of trust relationships in the development of algorithmically mediated systems and a methodology for deriving a "trust index" for online platforms that allows users to assess the trustability of platforms easily. The project is led by the University of Oxford in collaboration with the Universities of Edinburgh and Nottingham. Edinburgh develops novel computational techniques to evaluate and critique the values embedded in algorithms, and a prototypical AI-supported platform that enables users to exchange opinions regarding algorithm failures and to jointly agree on how to "fix" the algorithms in question to rebuild trust. The Oxford and Nottingham teams develop methodologies that support the user-centred and responsible development of these tools. This involves studying the processes of trust breakdown and rebuilding in online platforms, and developing a Responsible Research and Innovation approach to understanding trustability and trust rebuilding in practice. A carefully selected set of industrial and other non-academic partners ensures ReEnTrust work is grounded in real-world examples and experiences, and that it embeds balanced, fair representation of all stakeholder groups.ReEnTrust will advance the state of the art in terms of trust rebuilding technologies for algorithm-driven online platforms by developing the first AI-supported mediation and conflict resolution techniques and a comprehensive user-centred design and Responsible Research and Innovation framework that will promote a shared responsibility approach to the use of algorithms in society, thereby contributing to a flourishing Digital Economy.