Multiagent trust modeling for trusted AI and improved online social networks

用于可信人工智能和改进的在线社交网络的多代理信任建模

• 批准号: RGPIN-2021-02389
• 负责人: Cohen, Robin
• 金额: $ 4.66万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=750189
• 关键词: Multiagent; trust; modeling; trusted; AI

For over 20 years, artificial intelligence (AI) multiagent systems researchers have explored how best to model trustworthiness of agents, of value in selecting the most valuable partners for such applications as e-commerce. Methods for determining the most reputable agents have considered predicting future reliability based on past behaviour; there has also been effort in reasoning about the trustworthiness of peer advice about other agents. More recently, with increased attention on AI from organizations and individuals, the issue of trusted AI has come into focus as well: developing strategies for encouraging acceptance of AI solutions, allaying concerns and mistrust that may arise. The central premise of our research is that methods from multiagent trust modeling may hold the key in designing approaches for ensuring trusted AI. We will illustrate how lack of trust modeling handicaps general AI solutions and how the methods for calibrating trust-based models can be useful for assessing the value of varied efforts in trusted AI. Three key subgoals will be explored: mapping a solution for performing context-specific trust modeling; applying trust modeling to the critical social concern of digital misinformation; demonstrating how trust can serve to produce a more accessible and acceptable online networking environment for all users, understanding as well the role that opinion dynamics plays in bringing these communities together. Some primary methods used in our work will include Markov Decision Processes for progressively learning about trust from first principles, predicting future trustworthiness from past behaviour, integrating as well user modeling; simulations of trust modeling methods for calibrating and comparing, as is also done with the work on trust modeling testbeds; attuning solutions to user preferences including for assistive needs of users (older adults or those with some kind of impairment); reasoning about core groups of influencers in social networks through network dynamics; detecting digital misinformation through the modeling of authors and message raters, using a data-driven multi-faceted approach. This work will advance multiagent trust modeling, integrating Bayesian and data-driven methods and highlighting uses towards which trust is put. The impact of this work will be to enable significant steps forward with engendering trust in AI solutions through improved trust modeling methods. In Canada, organizations and individuals invested in seeing the benefits of AI will acquire better confidence in continuing to embrace these solutions. Users of social media challenged by questionable or overwhelming content will also emerge with a better footing. All of these considerations are ones with significant social value; the research agenda is one which should attract important participation from students of currently underrepresented groups, who will be sought for their unique expertise in AI and social networks.

20多年来，人工智能（AI）多基因系统研究人员探索了如何最好地模拟代理商的可信度，并在为电子商务等应用中选择最有价值的合作伙伴时有价值。确定最信誉良好的代理的方法已考虑基于过去的行为来预测未来的可靠性；关于对其他代理商的同伴建议的可信赖性，也付出了努力。最近，随着组织和个人对AI的关注，受信任的AI问题也开始关注：制定鼓励接受AI解决方案的策略，消除可能出现的关注和不信任。我们研究的主要前提是，来自多基金信任建模的方法可能是设计确保受信任人工智能的方法的关键。我们将说明缺乏信任建模障碍如何一般AI解决方案以及校准基于信任的模型的方法如何有助于评估受信任的AI中各种努力的价值。将探索三个关键子目标：映射用于执行特定上下文特定信任建模的解决方案；将信任建模应用于数字错误信息的关键社会关注；展示信任如何为所有用户提供更容易访问和可接受的在线网络环境，并了解动态在将这些社区汇集在一起​​的作用。我们工作中使用的一些主要方法将包括马尔可夫决策过程，用于从第一原则逐步学习信任，从而预测过去行为的未来信任度，并整合用户建模；对信任建模方法进行校准和比较的仿真，这也是对信任建模测试床上的工作；将解决方案调整为用户偏好的解决方案，包括对用户的辅助需求（老年人或某种损害的人）的辅助需求；通过网络动态对社交网络中的核心影响者群体的推理；使用数据驱动的多方面方法，通过对作者和消息评估者的建模来检测数字错误信息。这项工作将推动多基金会建模，整合贝叶斯和数据驱动的方法以及突出显示信任的用途。这项工作的影响将是通过改进的信任建模方法来实现对AI解决方案的信任，从而实现重大步骤。在加拿大，组织和个人投资以了解AI的好处将获得更好的信心，以继续采用这些解决方案。受到可疑或压倒性内容挑战的社交媒体用户也会以更好的基础出现。所有这些考虑因素都是具有重大社会价值的考虑因素。研究议程是应吸引当前代表性不足的团体学生的重要参与，他们将因其在AI和社交网络中的独特专业知识而寻求。