Counterweights and Complementarities: The Convergence of AI and Blockchain Powering a Decentralized Future
Organizations: Emily · University of Scranton · Marywood University · Nancy ·
Abstract
This editorial addresses the critical intersection of artificial intelligence (AI) and blockchain technologies, highlighting their contrasting tendencies toward centralization and decentralization, respectively. While AI, particularly with the rise of large language models (LLMs), exhibits a strong centralizing force due to data and resource monopolization by large corporations, blockchain offers a counterbalancing mechanism through its inherent decentralization, transparency, and security. The editorial argues that these technologies are not mutually exclusive but possess complementary strengths. Blockchain can mitigate AI's centralizing risks by enabling decentralized data management, computation, and governance, promoting greater inclusivity, transparency, and user privacy. Conversely, AI can enhance blockchain's efficiency and security through automated smart contract management, content curation, and threat detection. The core argument calls for the development of ``decentralized intelligence'' (DI) -- an interdisciplinary research area focused on creating intelligent systems that function without centralized control.
Figures & tables
| Research Question | Potential Implications and Mechanisms |
|---|---|
| How can blockchain-enabled decentralized AI marketplaces address the concentration of power in AI? | Decentralized marketplaces could empower smaller developers, fostering innovation and diversity by bypassing corporate gatekeepers. Blockchain may also enhance federated learning by securing updates and ensuring data provenance, enabling distributed training ( Ben-Sasson et al., 2014 ; McMahan et al., 2017 ) . |
| How can blockchain mitigate data monopolization? | Blockchain-based data cooperatives and DAOs could allow individuals to collectively own and manage data, challenging large corporations and promoting fairer data-sharing ( Hassan and De Filippi, 2021 ) . Data marketplaces on the blockchain could facilitate direct data sharing/sales with AI developers ( Wirth and Kolain, 2018 ) . |
| What is blockchain’s potential to lower economic and competitive barriers to AI innovation? | Tokenized access to computing resources, facilitated by blockchain, could democratize AI model training/deployment. Micropayments via blockchain could introduce new business models, lowering entry barriers ( Swan, 2015 ) . |
| How can blockchain mitigate AI-driven privacy erosion? | Blockchain-based decentralized identity (DID) systems and verifiable credentials could give individuals more control over their data ( Tobin and Reed, 2017 ) . Combining blockchain with zero-knowledge proofs (ZKPs) can further enhance privacy in AI data verification ( Ben-Sasson et al., 2014 ) . |
| How might NFTs help establish provenance and authenticity for human-created content in the age of AI-generated media? | NFTs could establish provenance and authenticity for human-created content, preserving its value. Decentralized, blockchain-based platforms could directly connect creators and audiences, reshaping the creative economy ( Wang et al., 2021 ; Atzori, 2015 ) . |
AI Governance
Artificial intelligence governance studies how laws, institutions, and technical controls shape AI development, deployment, accountability, and social effects.
See all 384 papers