AI and Greenwashing: Digital Transformation in Reducing Information Asymmetry
DOI:
https://doi.org/10.24256/kharaj.v8i2.10867Keywords:
Artificial Intelligence, Greenwashing, Asimetri Informasi, ESG, Transformasi Digital, Sustainability Reporting.Abstract
This research is motivated by the increasing practice of greenwashing amid the increasing demands for transparency in sustainability and ESG reporting. The high asymmetry of information between companies and stakeholders causes environmental claims to often not match the company's actual performance, thereby lowering the credibility of sustainability reporting systems. This study aims to analyze the role of Artificial Intelligence (AI) as a digital transformation mechanism in reducing information asymmetry and suppressing greenwashing practices. The method used is Systematic Literature Review (SLR) with a PRISMA approach to reputable scientific articles obtained from the Scopus database, Web of Science, ScienceDirect, Emerald Insight, IEEE Xplore, and Google Scholar in the period 2010–2025. The analysis process is carried out through thematic synthesis to identify patterns, mechanisms, and conceptual relationships between researches. The results show that AI plays a significant role in increasing the transparency of ESG reporting through real-time data analysis, anomaly detection, machine learning, large language models, and the integration of big data based on the Internet and IoT. AI has also been proven to be able to improve operational efficiency, strengthen corporate governance, and narrow the opportunities for environmental information manipulation. In addition, the development of digital technologies such as blockchain, cloud computing, and the Internet of Things strengthens the effectiveness of AI in creating a more accurate and transparent sustainability monitoring system. This study concludes that AI is an effective digital governance mechanism in reducing information asymmetry and greenwashing, although its implementation still requires regulatory support, good data quality, and adequate digital infrastructure.
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