Implementation of an explanation feature for the decision-making AI

If AI is used to make decisions, I believe we should also implement a feature that allows the AI itself to explain the reasoning behind its decisions. In practice, while the AI is making the decisions, there appear to be cases where even customer support staff do not fully understand why a particular decision was reached.

Therefore, introducing a mechanism that allows the AI itself to explain the reasons for its decisions would make it easier for users to understand and accept the results. It would also enable us to specifically identify what needs to be improved to resolve issues, which would help reduce unnecessary confusion and dissatisfaction.

Furthermore, there are significant benefits for the operators as well. Even in the unlikely event of an incorrect decision, analyzing the explanation provided by the AI makes it easier to identify where the problem occurred, which can be used to improve the decision-making logic and prevent recurrence.

Moreover, if such explanatory features are fully developed, the need for customer support to handle individual cases will decrease. Since users can receive explanations directly from the AI, this will also lead to reductions in response costs and time. As a result, this will become a highly efficient system that benefits both users and the platform operators.