EmeraldMind: A Knowledge Graph-Augmented Framework for Greenwashing Detection
This addresses the challenge of greenwashing for stakeholders relying on AI-driven sustainability assessments, though it is incremental as it builds on existing retrieval-augmented generation methods.
The paper tackles the problem of detecting misleading corporate sustainability claims (greenwashing) by introducing EmeraldMind, a framework that integrates a domain-specific knowledge graph with retrieval-augmented generation, achieving competitive accuracy, greater coverage, and superior explanation quality compared to generic LLMs on a new dataset.
As AI and web agents become pervasive in decision-making, it is critical to design intelligent systems that not only support sustainability efforts but also guard against misinformation. Greenwashing, i.e., misleading corporate sustainability claims, poses a major challenge to environmental progress. To address this challenge, we introduce EmeraldMind, a fact-centric framework integrating a domain-specific knowledge graph with retrieval-augmented generation to automate greenwashing detection. EmeraldMind builds the EmeraldGraph from diverse corporate ESG (environmental, social, and governance) reports, surfacing verifiable evidence, often missing in generic knowledge bases, and supporting large language models in claim assessment. The framework delivers justification-centric classifications, presenting transparent, evidence-backed verdicts and abstaining responsibly when claims cannot be verified. Experiments on a new greenwashing claims dataset demonstrate that EmeraldMind achieves competitive accuracy, greater coverage, and superior explanation quality compared to generic LLMs, without the need for fine-tuning or retraining.