As artificial intelligence (AI) technology becomes prevalent, “AI Wash”—the practice of firms exaggerating their AI capabilities to meet market expectations—has drawn increasing concern, posing a potential threat to capital market efficiency and the health of the technological ecosystem. This study focuses on the Board Interlock Network (BN), a key informal governance mechanism, to investigate its efficacy in governing corporate AI Wash. Using a sample of Chinese A-share listed companies from 2007 to 2022 and employing a Double Machine Learning (DML) model for causal inference, we find that the BN significantly inhibits corporate AI Wash. This finding remains robust after a series of robustness and endogeneity tests. Mechanism tests provide strong support for our core argument: the BN inhibits AI Wash by significantly enhancing both the breadth (number of reports) and depth (length of reports) of media attention. This heightened media scrutiny amplifies the potential reputational costs and exposure risks associated with opportunistic disclosure, thereby effectively inhibiting such behavior. Further heterogeneity analysis aligns with our theoretical predictions: the inhibitory effect of board interlocks is more pronounced in firms characterized by greater information asymmetry (e.g., high-technology firms), more complex governance needs (e.g., state-owned enterprises), and stronger external monitoring pressures (e.g., intense market competition). The core contribution of this study lies in revealing a synergistic governance effect between internal governance (interlocking directorates) and external information intermediaries (the media). It provides a novel pathway and empirical evidence for understanding how the BN can inhibit speculative behavior in emerging technology sectors by shaping the corporate information environment.
Wu et al. (Sat,) studied this question.