IncentiveChain: Blockchain-Enabled Reward Allocation and Trust Verification for Multi-Stakeholder AI Advertising Platforms
Keywords:
IncentiveChain, blockchain, reward allocation, trust verification, multi-stakeholder, AI advertising, smart contracts, decentralized governanceAbstract
The rapid evolution of artificial intelligence in digital advertising has created complex multi-stakeholder ecosystems wherein advertisers, publishers, AI model providers, data curators, and end users interact under conditions of asymmetric information and conflicting incentives. Existing platforms often suffer from opaque reward distribution, untraceable attribution of content generation, and insufficient mechanisms for verifying the trustworthiness of AI-driven contributions. This paper introduces IncentiveChain, a blockchain-enabled framework designed to address these challenges through transparent reward allocation and decentralized trust verification. IncentiveChain leverages smart contracts to automate and audibly distribute incentives based on verifiable contributions, while integrating decentralized identity and reputation systems to enable trust scoring across stakeholders. The architecture emphasizes structural trade-offs among scalability, privacy, and governance, and considers sustainability through tokenomics and on-chain governance mechanisms. A detailed analysis of deployment scenarios, robustness to malicious behavior, and policy implications is provided, drawing comparisons with existing centralized and federated approaches. The paper contributes a system-level perspective on how blockchain infrastructure can re-align incentives in AI advertising, fostering fairness, accountability, and long-term platform viability. Case illustrations from programmatic advertising and content moderation demonstrate the practical applicability of the proposed framework. The findings suggest that while blockchain-based reward allocation introduces overhead and regulatory complexity, the benefits in transparency and stakeholder trust justify its adoption for high-value, multi-party advertising ecosystems.
References
1. Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. Retrieved from https://bitcoin.org/bitcoin.pdf
2. Buterin, V. (2014). A next-generation smart contract and decentralized application platform. Ethereum White Paper.
3. Christidis, K., & Devetsikiotis, M. (2016). Blockchains and smart contracts for the internet of things. IEEE Access, 4, 2292-2303.
4. Kshetri, N., & Voas, J. (2018). Blockchain-enabled applications in artificial intelligence. Computer, 51(12), 108-112.
5. Zyskind, G., & Nathan, O. (2015). Decentralizing privacy: Using blockchain to protect personal data. In 2015 IEEE Security and Privacy Workshops (pp. 180-184). IEEE.
6. Moura, J., & Gomes, D. (2017). Blockchain-based reputation systems: A survey. In 2017 International Conference on Intelligent Systems (pp. 123-128). IEEE.
7. Shi, C., Li, S., Lu, W., Wu, W., Wang, C., Cheng, Z., ... & Chua, T. S. (2026). TraceRouter: Robust Safety for Large Foundation Models via Path-Level Intervention. arXiv preprint arXiv:2601.21900.
8. Miers, I., Garman, C., Green, M., & Rubin, A. D. (2013). Zerocoin: Anonymous distributed e-cash from bitcoin. In 2013 IEEE Symposium on Security and Privacy (pp. 397-411). IEEE.9. Back, A., Corallo, M., Dashjr, L., Friedenbach, M., Maxwell, G., Miller, A., ... & Wuille, P. (2014). Enabling blockchain innovations with pegged sidechains. Retrieved from https://blockstream.com/sidechains.pdf
10. Poon, J., & Dryja, T. (2016). The bitcoin lightning network: Scalable off-chain instant payments. Retrieved from https://lightning.network/lightning-network-paper.pdf
11. Zhou, D. (2026). AI-Driven Hybrid SAST–DAST–SCA–IAST Framework for Risk-Based Vulnerability Prioritization in Microservice Architectures.
12. Wood, G. (2014). Ethereum: A secure decentralised generalised transaction ledger. Ethereum Project Yellow Paper, 151, 1-32.
13. Androulaki, E., Barger, A., Bortnikov, V., Cachin, C., Christidis, K., De Caro, A., ... & Yellick, J. (2018). Hyperledger fabric: A distributed operating system for permissioned blockchains. In Proceedings of the Thirteenth EuroSys Conference (pp. 1-15).
14. Zheng, Z., Xie, S., Dai, H., Chen, X., & Wang, H. (2017). An overview of blockchain technology: Architecture, consensus, and future trends. In 2017 IEEE International Congress on Big Data (pp. 557-564). IEEE.
15. Catalini, C., & Gans, J. S. (2016). Some simple economics of the blockchain. NBER Working Paper No. 22952.
16. Bonneau, J., Miller, A., Clark, J., Narayanan, A., Kroll, J. A., & Felten, E. W. (2015). SoK: Research perspectives and challenges for bitcoin and cryptocurrencies. In 2015 IEEE Symposium on Security and Privacy (pp. 104-121). IEEE.
17. Miller, A., & Bentov, I. (2017). Zerocash: Decentralized anonymous payments from bitcoin. In 2017 IEEE Symposium on Security and Privacy (pp. 459-474). IEEE.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Journal of Data Intelligence and AI Systems
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
