Artificial Intelligence for Greenwashing Detection: A Conceptual Analysis of NLP and LLM in Sustainability Reporting - Mohammad Mostaf Fauzil Mufti; Tiara Rizky Cahya; Zahwa Nura Aziza; Khristina Putri Kasihwigati; Maureen Cahayli; Dina Safitri; Diajeng Fitri Wulan (Hikamatzu | Journal of Multidisciplinary, 2026)
Greenwashing, the practice of making misleading environmental claims, continues to hinder genuine progress toward sustainable development. Studies show that a significant proportion of corporate sustainability claims are exaggerated or unfounded, creating a demand for effective tools to identify such practices. Traditional methods of detecting greenwashing, such as manual reviews and basic keyword analysis, are often insufficient due to the complexity and volume of data involved. This study uses a conceptual and analytical research design to summarize existing evidence on the use of Artificial Intelligence (AI), including Natural Language Processing (NLP) and Large Language Models (LLMs), in detecting greenwashing. By analyzing sustainability reports, press releases, and social media content, these AI tools offer a more efficient and accurate approach to identifying discrepancies between corporate claims and actual practices. The findings demonstrate that AI technologies can significantly advance greenwashing detection, contributing to more reliable and accessible sustainability assessments. However, limitations remain, as the study focuses on only two AI methodologies. Future research should explore a wider range of AI tools and techniques to address industry-specific challenges and regulatory concerns, ensuring a more comprehensive approach to detecting greenwashing in corporate practices.