The relationship between artificial intelligence, ESG disclosure and ‎accounting information quality

In recent years, corporate sustainability reporting has transitioned from voluntary disclosure to a ‎core component of corporate communication. Concurrently, the rapid advancement of Artificial ‎Intelligence (AI) and Natural Language Processing (NLP) has provided powerful tools to analyze ‎and benchmark these disclosures. This study investigates the relationship between artificial ‎intelligence, Environmental, Social, and Governance (ESG) disclosure quality, and accounting ‎information quality (AIQ) within the context of emerging markets, specifically focusing on ‎companies listed on the Tehran Stock Exchange (TSE). Drawing on disclosure theory, signaling ‎theory, and legitimacy theory, this study conceptualizes ESG disclosure quality across four ‎dimensions: readability, comparability, informativeness, and credibility. Furthermore, it examines ‎how AI-driven text analysis and machine learning tools can mitigate information asymmetry and ‎earnings management, thereby enhancing accounting information quality. Given the unique ‎institutional and linguistic (Persian) environment of the Iranian capital market, this research ‎addresses the transferability of global cyber-finance and AI methodologies to a localized setting ‎characterized by voluntary ESG reporting and high ownership concentration. The findings aim to ‎assist regulators, auditors, and investors in utilizing AI-assisted monitoring tools to detect ‎corporate greenwashing, identify accounting anomalies, and improve the overall transparency of ‎financial and non-financial reporting.‎

کلیدواژه ها:

Artificial Intelligence ، ESG Disclosure ، Accounting Information Quality ، Natural ‎Language Processing (NLP) ، Tehran Stock Exchange ، Information Asymmetry ، Impression ‎Management