Toward Interpretable Group Decision Making Using Crowdsourced AHP

Abstract

The remarkable development of AI technology has made it possible to support a variety of real-world decision-making problems. However, the power of AI is still limited for open questions with ambiguous definitions and with a limited amount of data. On the other hand, the method of solving problems through collective intelligence by calling for cooperation from many people via the Internet has been widely used in recent years, but this method has reliability problems. In order to increase the reliability of decisions made by increasingly complex AI systems, much research has been done to make AI decisions interpretable to humans; similarly, the interpretability problem of seeking a basis for judgment by unspecified collective intelligence arises here. In this research, we aim to solve this problem by using Analytic Hierarchy Process (AHP), which is used as a method to aid rational decision making, as a framework for collecting and evaluating answers and criteria using crowdsourcing.