Prototyping Agents for Resolving Opinion Biases Toward Facilitating Sublation of Conflict in Web-based Discussions

Abstract

The term “sublation” (or “aufheben”) refers to the process of arriving at an agreed upon answer to two opposing arguments without denying either of them. In this study, we conducted a discussion experiment in which we quantified the degree of sublation and analyzed the results to determine the factors that contribute to the cessation of conflicting opinions in discussions. Our findings revealed a weak positive correlation between the number of URLs posted as evidence for one's opinion and the degree of sublation of the consensus proposal. In actual discussions and debates, however, there are times when everyone makes the same argument, with little or no opposing views, resulting in biased opinions. To address this problem, we developed a method to eliminate bias in opinions, in which an agent posts information that reinforces the opinion of a minority in a discussion. The experimental results demonstrate that GPT-3, a natural language processing model, can be applied to summarization of relevant information for information provision and the resolution of opinion bias.