Integrating SIWEC and Koch snowflake fuzzy sets to prioritize trust factors in an artificial intelligence-based audit system

This study aims to identify effective strategies to increase confidence in AI-based audits. A novel decision-making model is being developed to identify these strategies. In this process, seven criteria are identified through a literature review. Furthermore, opinions on these criteria are obtained from 10 different subject-matter experts. The significance ratio for these people is computed based on their work experience. In this process, an artificial intelligence-based approach is taken into consideration. Furthermore, the weights of the selected criteria are determined using the SIWEC methodology. On the other hand, Koch snowflake fuzzy sets are introduced in this study to address uncertainty in decision-making analysis. Perceived change in audit quality (PCAQ) is the most important indicator, with a weight of 0.181. In addition to this issue, stakeholders’ acceptance and resistance to technology (SART) play a crucial role in this process, with a weight of 0.166. This study contributes to the literature by creating a novel model to identify prior strategies to improve trust in the AI-based audit systems. These findings pave the way to take appropriate actions to increase the effectiveness of this process.