Detecting Greenwashing through Artificial Intelligence: Methods and Challenges

Aman Vishnoi; Anjani Singh Chauhan; Mohammad Iftekhar Khan

doi:10.69968/txgz4525

Authors

Aman Vishnoi MBA, Finance Marketing, Jagran Lake City University, Chandanpura, Bhopal, Madhya Pradesh, INDIA
Anjani Singh Chauhan MBA, Finance Marketing, Jagran Lake City University, Chandanpura, Bhopal, Madhya Pradesh, INDIA
Mohammad Iftekhar Khan Ph.D., Jagran Lake City University, Chandanpura, Bhopal, Madhya Pradesh, INDIA

DOI:

https://doi.org/10.69968/txgz4525

Keywords:

Greenwashing, Artificial Intelligence (AI), Natural Language Processing (NLP), ESG (Environmental, Social, and Governance), Machine Learning (ML)

Abstract

Greenwashing has become one of the critical issues in the age of sustainability-driven business communication, in which companies might overestimate or misreport their environmental performance. The paper is a review of how Artificial Intelligence (AI) can be used to detect greenwashing through a recent development in machine learning, natural language processing, and large language models. It notes some of the major methods including sentiment analysis, topic modeling, knowledge graphs and cross-source data validation in the process of identifying deceptive sustainability claims. The paper also looks at new technologies such as retrieval augmented generation and multimodal AI systems to have comprehensive ESG evaluation. Moreover, the paper presents the key challenges, such as data quality issues, absence of standardized datasets, model interpretability, and the dynamic nature of sustainability language. Literature review shows that AI-based detection is in its nascent stage with limited research. Generally, the paper highlights the importance of transparent, strong, and ethically sound AI frameworks to enhance the level of credibility in sustainability reporting.

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