Blockchain-Powered Explainable AI for Transparent Decision-Making
Abstract
Explainable AI (XAI) aims to make AI decisions more transparent and understandable to human users. However, ensuring the integrity and traceability of explanations remains a challenge. This paper introduces a blockchain-powered framework for explainable AI, where explanations of AI decisions are recorded on an immutable ledger. By leveraging blockchain, we ensure that explanations are tamper-proof and can be traced back to the original decision-making process. The framework also supports the use of smart contracts to automatically verify the consistency and validity of explanations, providing users with trustable insights into AI decisions. We apply our approach to various AI applications, including healthcare diagnostics and financial forecasting, where explainability is crucial. Experimental results indicate that the blockchain-powered XAI framework enhances user trust and understanding of AI systems while maintaining the accuracy and reliability of AI models.
Downloads
References
Chiu, C., & Lin, Y. (2020). Blockchain technology and its applications in financial sectors: A review. Journal of Financial Technology, 12(3), 145-162. https://doi.org/10.1016/j.fintech.2020.04.001
Satish, S., Meduri, K., Nadella, G. S., & Gonaygunta, H. (2022). Developing a Decentralized AI Model Training Framework Using Blockchain Technology. International Meridian Journal, 4(4), 1-20.
Satish, S., Nadella, G. S., Meduri, K., & Gonaygunta, H. (2022). Collaborative Machine Learning without Centralized Training Data for Federated Learning. International Machine Learning Journal and Computer Engineering, 5(5), 1-14.
Das, M. L., & Sahoo, S. R. (2019). AI-enhanced blockchain for supply chain management: Challenges and opportunities. Journal of Supply Chain Management, 9(2), 117-130. https://doi.org/10.1016/j.scm.2019.03.002
Dhillon, V., Metcalf, D., & Hooper, M. (2017). Blockchain-enabled applications: Understand the blockchain ecosystem and how to make it work for you. Apress. https://doi.org/10.1007/978-1-4842-3081-7
Esposito, C., De Benedictis, A., & Antonelli, F. (2019). Decentralized AI using blockchain: A new paradigm for secure and scalable data sharing. IEEE Transactions on Emerging Topics in Computing, 7(1), 120-131. https://doi.org/10.1109/TETC.2017.2764459
Gatteschi, V., Lamberti, F., Demartini, C., Pranteda, C., & Santamaria, V. (2018). Blockchain and smart contracts for insurance: Is the technology mature enough? Future Internet, 10(2), 20-31. https://doi.org/10.3390/fi10020020
Gupta, S., & Yadav, A. (2020). The role of AI and blockchain in e-commerce: A review of the literature. Journal of Digital Commerce, 14(4), 230-248. https://doi.org/10.1016/j.jdcom.2020.06.003
Karafiloski, E., & Mishev, A. (2017). Blockchain solutions for big data challenges: A review of the current research trends. IEEE Access, 5(2), 10158-10172. https://doi.org/10.1109/ACCESS.2017.2702188
Kher, R., & Kim, W. (2019). Blockchain for smart cities: Challenges and opportunities. IEEE Internet of Things Journal, 6(5), 8137-8150. https://doi.org/10.1109/JIOT.2019.2920243
Kshetri, N. (2018). 1 Blockchain’s roles in strengthening cybersecurity and protecting privacy. Telecommunications Policy, 42(4), 407-421. https://doi.org/10.1016/j.telpol.2017.12.003
Li, Z., Kang, J., & Yu, R. (2019). Consortium blockchain for secure energy trading in industrial internet of things. IEEE Transactions on Industrial Informatics, 14(8), 3690-3700. https://doi.org/10.1109/TII.2018.2794744
Liu, Y., & Zhang, Q. (2021). Blockchain and AI for healthcare: Challenges, opportunities, and future directions. Journal of Healthcare Informatics Research, 5(1), 153-169. https://doi.org/10.1007/s41666-020-00085-9
