Integrating Artificial Intelligence into Blockchain-Based Systemic Report Verification: Towards an AI Protocol for Systemic Analysis

Integrity and transparency of systemic reports are fundamental elements for ensuring the reliability of sustainability assessments, circular economy evaluations, and preventing instances of project greenwashing. In the research presented at RSD13 ("Applying Blockchain to Systemic Design: Ensuring Project Authenticity via Quantitative Report Verification"), it was demonstrated through case studies how blockchain technologies—utilizing notarization, smart contracts, and NFTs—can provide an immutable verification system for systemic reports. Building on this premise, this study introduces an evolution in the verification protocol by integrating Artificial Intelligence (AI), aimed at enhancing systemic design methodologies through the synergy of AI and blockchain verification systems. The proposed protocol leverages Machine Learning (ML) algorithms to identify anomalies, inconsistencies, and potential distortions in systemic reports, automating the verification process through real-time data analysis and pattern recognition via Optical Character Recognition (OCR). With advancements in computational technologies towards "Light Large Language Model (LLM)" generative AI models, which are less complex and fully open source compared to traditional LLMs, the project aims to secure the verification process within a completely safe environment. This approach allows training neural models exclusively with specific data, avoiding the need to upload sensitive information online, thereby ensuring greater data security. This new frontier enables harnessing all benefits of AI without compromising cybersecurity, a critical concern for safeguarding sensitive information. This research aims to develop a hybrid AI-blockchain model for report validation, where AI evaluates data integrity and blockchain ensures decentralized certification. It implements Natural Language Processing (NLP) techniques for analysing textual components of reports, ensuring compliance with major sustainability frameworks such as Global Reporting Initiative (GRI) and ISO14001, without being centralized under a single control. The objective is to define a standardized AI protocol for systemic design verification applicable across various sectors, ensuring more accurate and reliable sustainability reporting. Integrating AI into systemic design methodologies aims to introduce secondary verification processes complementary to blockchain, currently absent yet necessary to strengthen protection for designers and involved companies. Furthermore, it aims to offer a scalable, automated approach resistant to data validation fraud. The use of advanced AI tools not only optimizes process efficiency but also ensures greater compliance with sustainability standards, thereby enhancing stakeholder confidence and reducing the risk of misleading information dissemination. This research contributes to the fields of systemic design, data verification, and digital trust, proposing an innovative AI-based framework to ensure the authenticity of systemic design documentation.