Blockchain + AI for Transparent and Auditable AI Models

Abstract: As AI becomes deeply entrenched in mission-critical domains like healthcare, finance, and government, the need for reliable, explainable, and ethically compliant AI systems has increased immensely. Most traditional AI systems exist as opaque "black boxes" wherein it is not possible to see how decisions are being made or to verify compliance with rules and ethical requirements. This transparency issue makes it challenging to hold AI systems accountable and develop confidence in the outcomes produced by them. This work presents a new framework that marries the advantages of blockchain technology with explainable AI to produce transparent and auditable AI systems. The essential properties of blockchain decentralization, immutability, and automation of smart contracts are utilized to have tamper-proof records of the whole AI life cycle. This entails data gathering, preprocessing, and training of models, updates, and inference events. These logs create an unalterable audit trail that allows regulators, users, and stakeholders to confirm the integrity and fairness of the AI models at any given moment. Moreover, the framework incorporates explainable AI methods to produce human-interpretable explanations of model outputs. This not only enhances transparency but also enables stakeholders to determine whether AI judgments are reasonable and unbiased. We provide a prototype implementation of this framework and compare its performance in a real-world case study in the healthcare industry. The findings show that the integrated system effectively strengthens traceability, establishes trust, and facilitates regulatory compliance without any decline in the performance of AI models. In summary, this study demonstrates how the integration of blockchain and AI closes essential gaps in transparency and accountability, paving the way for the responsible and ethical use of AI. The framework outlined provides a pragmatic way forward for companies wishing to implement AI technologies without diminishing public trust and fulfilling legal requirements.