WSEAS Transactions on Business and Economics | 2026
Authors: Sgantzos K.; Ferrara M.
DOI: 10.37394/23207.2026.23.11
Journal: WSEAS Transactions on Business and Economics
Year: 2026
Publisher: World Scientific and Engineering Academy and Society
Document Type: Article
Open Access: All Open Access; Gold Open Access
Cited by: 0
In the contemporary landscape of artificial intelligence (AI) and machine learning (ML), the integrity, diversity and quality of training datasets are critical for ensuring the accuracy and reliability of predictive models. However, the phenomenon of big-data pollution, manifested through AI-generated synthetic data, inconsistencies, biases, and data poisoning within datasets, undermines model performance by diminishing the Shannon Entropy of the system. This study proposes a novel framework that integrates the Dataset Core approach with tokenized data, triple-entry accounting (TEA), and distributed ledger technology (DLT) to address these challenges. Our Dataset Core method preserves essential information value while filtering out potentially harmful elements, providing mathematically grounded protection against data pollution. Combined with blockchain-based verification, this approach establishes a foundation for enhanced transparency and trustworthiness in AI applications, with significant implications for sectors such as finance, healthcare, and beyond. © 2026, World Scientific and Engineering Academy and Society. All rights reserved.
AI deterioration; Blockchain; Data Poisoning; Dataset Core; Distributed Ledger Technology; Machine Learning Security; Shannon Entropy; Triple-Entry Accounting