Regulatory Approaches to AI Auditing in Asia: Promoting Fairness and Mitigating Bias

This paper examines the regulatory landscape that controls AI fairness and bias in the financial auditing domains in five major Asian jurisdictions (China, Singapore, South Korea, Japan and India), which aims to identify gaps and provide the recommendations to ensure ethical AI deployment in auditing. Considering the fact that artificial intelligence is being actively integrated in the field of financial auditing, it is crucial to have a full overview of the corresponding regulatory frameworks and align them, which will allow the deployment of artificial intelligence ethically. The study has employed a qualitative, comparative legal and policy analysis to map and evaluate the current standards, laws, and official guidelines related to AI fairness in auditing within selected countries. Analysis reveals a dynamic but fragmented regulatory environment. While the theory of AI fairness is widely accepted, nations have adopted quite different regulatory strategies. These range from binding laws in countries such as China and South Korea to voluntary outlines in Singapore and Japan, while others, such as India, rely on adopting existing laws. However, a significant difference in all these jurisdictions is clear: the absence of specific, applied rules for auditing of AI systems. This deficiency highlights several major challenges, including defining fairness in an audit context, ensuring data quality, balancing transparency with intellectual property rights and developing the necessary institutional capacity. As a result, the current framework is considered insufficient to address the practical demands of AI Fairness Auditing.