Abstract
Greenwashing, i.e., the misrepresentation of environmental, social, and governance (ESG) performance, poses major challenges for regulators, investors, and the public. As sustainability reporting becomes central to corporate accountability, regulatory scrutiny underscores the need for reliable mechanisms to detect misleading claims. Existing AI tools are either opaque “black boxes” or lack regulatory and contextual sensitivity. This study applies a Design Science Research (DSR) methodology to develop an AI-enabled artefact that detects greenwashing in sustainability reports. Grounded in the Product and Service Performance Information Quality (PSP/IQ) model, the artefact incorporates explainable AI techniques such as natural language processing, anomaly detection, and semantic similarity analysis to evaluate ESG disclosures against standards and stakeholder expectations. The research contributes design principles, architecture, and evaluation criteria for transparent, regulator-aligned detection tools. Outcomes include enhanced credibility, accountability, and compliance in ESG reporting, advancing both academic knowledge and practical solutions to greenwashing in Australia and beyond.
Recommended Citation
Teoh, Say Yen; Xie, Yancong; Nguyen, Cong Kha; and Modest, Stephan Dua, "Designing an AI Artefact to Detect Greenwashing in
Sustainability Reports: A Design Science Research Method" (2025). ACIS 2025 Proceedings. 144.
https://aisel.aisnet.org/acis2025/144