Blockchain and AI Convergence: Creating Explainable, Auditable, and Immutable Data Ecosystems

Abstract

In today’s digital landscape, trust and transparency are critical for reliable and accountable AI systems. The integration of Blockchain with Artificial Intelligence (AI) offers a novel approach to ensuring data integrity, auditability, and ethical decision-making. This paper presents a hybrid architecture that combines Blockchain with AI to build transparent and immutable data models by leveraging smart contracts, decentralized storage via IPFS, and explainable AI methods to enhance data provenance and system accountability. Experiments were conducted using synthetic data and publicly available datasets, including the UCI Healthcare dataset and a financial dataset from Kaggle. Random Forest and Convolutional Neural Networks (CNNs) were implemented using PyTorch, while the Blockchain environment was simulated using the Ethereum testnet. Evaluation metrics included accuracy, latency, trust index, immutability score, and explainability coverage. Results indicate that while Blockchain integration introduces slight latency, it significantly boosts system trust and transparency. Accuracy experienced a minimal drop from 89.2% to 88.7% due to logging overhead. However, the Trust Index rose sharply from 0.42 to 0.93, Explainability Coverage increased from 18% to 91%, and the Immutability Score remained at 100%, validating Blockchain’s role in securing AI decisions. These outcomes demonstrate that the trade-off in performance is negligible compared to the substantial benefits in transparency and integrity. The proposed integrated framework thus offers a scalable and responsible solution for deploying AI in high-stakes domains such as healthcare, finance, and supply chains, where data security, trustworthiness, and explainability are essential for long-term adoption and regulatory compliance.