Decentralized Machine Learning Governance: Overview, Opportunities, and Challenges
Dana Alsagheer, Lei Xu, Weidong Shi
Abstract
Researchers have started to recognize the necessity for a well-defined ML governance framework based on the principle of decentralization and comprehensively defining its scope of research and practice due to the growth of machine learning (ML) research and applications in the real world and the success of blockchain-based technology. In this paper, we study decentralized ML governance, which includes ML value chain management, decentralized identity for the ML community, decentralized ownership and rights management of ML assets, community-based decision-making for the ML process, decentralized ML finance, and risk management.
Topics & Concepts
DecentralizationCorporate governanceScope (computer science)BlockchainProcess (computing)Computer scienceProcess managementRisk managementKnowledge managementBusinessComputer securityFinanceEconomicsProgramming languageMarket economyOperating systemBlockchain Technology Applications and SecurityPrivacy-Preserving Technologies in DataBig Data and Business Intelligence