Evaluating Multimodal AI for Greenwashing Detection: A Comparative Analysis of ChatGPT, Claude, and Gemini in ESG Reports

The rapid expansion of sustainability reporting under the EU Corporate Sustainability Reporting Directive (CSRD) has intensified concerns about greenwashing, particularly in visual communication within ESG reports. Recent advances in multimodal artificial intelligence offer new possibilities for automated detection, yet their reliability in non-English corporate reporting contexts remains unclear. This study evaluates the greenwashing detection capabilities of three leading multimodal AI systems—ChatGPT 5.1, Claude 4.5 Sonnet, and Gemini 2.5 Flash—using a purposively selected sample of 20 Polish ESG reports benchmarked against ESRS-aligned performance scores from the national “Ranking ESG”. A standardized auditing prompt was applied across all tools to generate comparable assessments of visual greenwashing. Contrary to theoretical expectations and all four hypotheses, the models did not demonstrate negative correlations between performance and AI-detected greenwashing; instead, high-performing firms frequently received higher greenwashing scores. Dimensional analyses showed inconsistent and often contradictory evaluations across Environmental, Social, and Governance pillars, while inter-tool reliability proved extremely low (Krippendorff’s α ≈ 0). These findings indicate that current multimodal AI systems conflate communication sophistication with deceptive intent and lack sufficient contextual understanding for ESG assurance. The study highlights significant methodological limitations and outlines directions for developing domain-specific, ESRS-aligned AI tools for greenwashing detection.