Decentralized Science Artificial Intelligence (DeSci AI): Barriers and Opportunities for Mass Adoption by the Scientific Community
Year: 2025
Type: preprint
Abstract: : The emergence of decentralized science (DeSci) and the potential for its acceleration through artificial intelligence (AI) presents both remarkable promise and formidable challenges for modern research. DeSci AI platforms use blockchain-based, openly governed workflows and autonomous AI “swarms” capable of literature synthesis, peer-review triage, and reputation tracking to democratize access, speed discovery, and rebuild public trust. Yet, they also introduce complex implementation barriers and governance questions. The transformative claims of DeSci must be gauged against the benchmark of conventional science. Traditional research practice is anchored in methodological rigor through hypothesis-driven design, validated instrumentation, standard operating protocols, and independent peer review. Stable institutional arrangements such as universities, funding councils, and archival journals further buttress reliability by providing clear governance and normative oversight. Nevertheless, the contemporary academic reward economy, in which publication is highly valued, and grant income constitute the currency of promotion, often favors projects with near-certain, publishable outcomes. This frequently suppresses genuine curiosity, leaving potentially transformative inquiries chronically underexplored. The scientific community must therefore embrace decentralized, data-secure alternatives while preserving valuable institutional frameworks. Successful adoption of DeSci-AI hinges on interrelated technical, cultural, regulatory, and resource-related challenges. This perspective maps those barriers and proposes integrated strategies including token-backed reviewer remuneration, incentive pools for negative or replication studies, AI-driven misconduct auditors, and hybrid governance pilots, to chart a realistic pathway for the scientific community to leverage DeSci AI synergies while safeguarding rigor, equity, and societal trust.
Source: OSF Preprints (OSF Preprints)
Authors: Sultan, Ahmed, Jessri, Maryam, Janjua-Schick, Hannah, Hemingway, Andrew, Tavares, Tiffany
Language: English
Subfield: Information Systems and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W6925414154
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