Artificial Intelligence and Accounting Information Quality: Causal Inference Based on Double Machine Learning

Abstract

Against the backdrop of the expanding digital economy, artificial intelligence, as a key technology underpinning corporate digital transformation, is increasingly influencing corporate governance practices and firms’ financial behavior. Using data from Chinese A-share listed firms from 2016 to 2024, this study applies a double machine learning approach to investigate the effect of AI on corporate accounting information quality and to identify the mechanisms underlying this relationship. The empirical results indicate that greater AI application significantly improves accounting information quality. This effect operates primarily through three channels: reducing operational risk, easing financing constraints, and mitigating agency costs. Further analysis reveals that the improvement in accounting information quality associated with AI is stronger for firms located in regions with higher levels of marketization and more developed digital infrastructure, as well as for firms facing less intense market competition. By examining accounting information quality as an important economic consequence of AI adoption, this study broadens the existing literature on the governance effects of AI and provides additional empirical evidence on the channels through which AI contributes to higher-quality accounting information. The findings also provide practical implications for policymakers seeking to improve the implementation of the “AI Plus” initiative and for firms pursuing digital transformation alongside improvements in intelligent governance.