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| Formato: | Recurso digital |
| Lenguaje: | inglés |
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Zenodo
2020
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| Acceso en línea: | https://doi.org/10.5281/zenodo.17875557 |
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| _version_ | 1866901826567143424 |
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| author | Rashmi K. Nair |
| author_facet | Rashmi K. Nair |
| contents | The evolution of Artificial Intelligence (AI) from centralized models toward decentralized architectures has fundamentally reshaped the paradigms of data management, ownership, and governance. In traditional AI ecosystems, data is consolidated within centralized repositories for model training and analytics, resulting in challenges related to privacy, latency, and compliance with regulatory frameworks. Decentralized AI architectures encompassing federated learning, edge AI, swarm intelligence, and blockchain-based frameworks offer a transformative alternative that aligns technological innovation with distributed data governance principles. These architectures enable AI systems to learn collaboratively across multiple nodes or organizations without transferring raw data, ensuring data sovereignty and compliance with global data protection mandates such as GDPR and CCPA. This review examines how decentralized AI architectures influence distributed data governance by promoting transparency, trust, and accountability in multi-party data ecosystems. The integration of AI with blockchain and distributed ledger technologies provides immutable audit trails and decentralized identity management, enabling verifiable governance across federated networks. Moreover, privacy-preserving techniques such as differential privacy, homomorphic encryption, and secure multiparty computation empower organizations to perform analytics on encrypted datasets while maintaining compliance with ethical and legal data-handling standards. Through a synthesis of academic research and real-world applications, the review highlights the significant advantages of decentralized AI, including enhanced privacy assurance, reduced systemic risks, and improved collaboration among data stakeholders. However, the transition toward decentralized intelligence introduces new challenges related to interoperability, communication overhead, and model convergence in distributed environments. Ensuring fairness, accountability, and explainability within federated systems remains a critical governance issue, as decentralized decision-making increases complexity in auditing and oversight. Furthermore, the governance of AI models themselves rather than just data poses emerging regulatory and ethical questions in globally interconnected ecosystems. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17875557 |
| institution | Zenodo |
| language | eng |
| publishDate | 2020 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | The Influence Of Decentralized AI Architectures On Distributed Data Governance Rashmi K. Nair The evolution of Artificial Intelligence (AI) from centralized models toward decentralized architectures has fundamentally reshaped the paradigms of data management, ownership, and governance. In traditional AI ecosystems, data is consolidated within centralized repositories for model training and analytics, resulting in challenges related to privacy, latency, and compliance with regulatory frameworks. Decentralized AI architectures encompassing federated learning, edge AI, swarm intelligence, and blockchain-based frameworks offer a transformative alternative that aligns technological innovation with distributed data governance principles. These architectures enable AI systems to learn collaboratively across multiple nodes or organizations without transferring raw data, ensuring data sovereignty and compliance with global data protection mandates such as GDPR and CCPA. This review examines how decentralized AI architectures influence distributed data governance by promoting transparency, trust, and accountability in multi-party data ecosystems. The integration of AI with blockchain and distributed ledger technologies provides immutable audit trails and decentralized identity management, enabling verifiable governance across federated networks. Moreover, privacy-preserving techniques such as differential privacy, homomorphic encryption, and secure multiparty computation empower organizations to perform analytics on encrypted datasets while maintaining compliance with ethical and legal data-handling standards. Through a synthesis of academic research and real-world applications, the review highlights the significant advantages of decentralized AI, including enhanced privacy assurance, reduced systemic risks, and improved collaboration among data stakeholders. However, the transition toward decentralized intelligence introduces new challenges related to interoperability, communication overhead, and model convergence in distributed environments. Ensuring fairness, accountability, and explainability within federated systems remains a critical governance issue, as decentralized decision-making increases complexity in auditing and oversight. Furthermore, the governance of AI models themselves rather than just data poses emerging regulatory and ethical questions in globally interconnected ecosystems. |
| title | The Influence Of Decentralized AI Architectures On Distributed Data Governance |
| url | https://doi.org/10.5281/zenodo.17875557 |