Exploring the potential impacts of machine learning on trust in fishery management

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.

Automated summary

Trust and collaboration are crucial for effective fishery management, and the application of machine learning provides new opportunities. However, challenges arise from the changes in monitoring and data analysis, as well as the impact on forms of trust between stakeholders.