Multiagent trust modeling for trusted AI and improved online social networks

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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.