Recent literature and empirical research show that both trust and collaboration are of great importance for effective fishery management. The application of Machine Learning (ML) to fishery management offers exciting new opportunities for data synthesis and analysis and integrated insights across typically siloed domains. Yet, challenges remain as ML approaches provide new means of monitoring, enforcement and data analysis. Trust is among the underlying bases of collaboration, and control is the main means of shaping collaborative decision-making techniques. As ML changes the dynamics of governance and enhances management control mechanisms, ML affects trust. ML methods are being introduced into a context that suffers a lack of transparency and trust between fishers and managers. As ML technologies continue to be used to inform fishery management and influence knowledge sharing and communication within the fishery network, forms of trust existing in the management network will be impacted differently. This article provides a concise review of a subset of potential ML applications to fishery management to explore how these emerging methods may impact forms of trust between fishery stakeholders.

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探索机器学习对渔业管理信任的潜在影响

最近的文献和实证研究表明，信任和合作对于有效的渔业管理都非常重要。机器学习 (ML) 在渔业管理中的应用为数据合成和分析以及跨典型孤立领域的综合见解提供了令人兴奋的新机会。然而，随着机器学习方法提供了新的监控、执行和数据分析手段，挑战依然存在。信任是协作的基础，而控制是塑造协作决策技术的主要手段。随着 ML 改变治理的动态并增强管理控制机制，ML 会影响信任。ML 方法被引入到渔民和管理者之间缺乏透明度和信任的环境中。随着机器学习技术继续用于为渔业管理提供信息并影响渔业网络内的知识共享和交流，管理网络中存在的信任形式将受到不同的影响。本文简要回顾了渔业管理中潜在的机器学习应用子集，以探讨这些新兴方法如何影响渔业利益相关者之间的信任形式。