Use cases, challenges, and opportunities of AI-based tools for reducing bycatch and discards in fisheries

The use of Artificial Intelligence (AI) to manage bycatch and discards is a growing interest in fisheries. However, implementing AI in fisheries faces challenges despite its promised benefits. This study evaluates the applications, challenges, and opportunities of adopting AI to reduce bycatch and discards using the Technology-Organization-External business environment (TOE) framework. We reviewed literature to identify projects using AI to manage bycatch and discards, conducted 17 in-depth interviews with fishery stakeholders, and supplemented these insights by attending two fishery innovation-related events. We identified several use cases of AI in managing bycatch and discards, including real-time species identification during fishing and catch registration, on-board automated sorting of species via conveyor belt and robotics, integration of AI with smart fishing gear, environmental-DNA sampling, and data-driven decision making in fishing. Several challenges hinder the adoption of AI-based tools in fisheries. First, most use-cases are at developmental, pilot, or prototype stage, suggesting the technology is not yet fully mature. Second, there is limited acceptance of AI-based tools among fishers due to concerns about trust, privacy, security, lack of incentives, and human replacement. Third, the need for high-quality big data and the complexity of modelling ecological systems make AI-models questionable. This study highlights three recommendations: (1) cross-sector collaboration to promote trust, support science-based policies and decisions, and make AI-based tools more feasible for fishers; (2) combining economic instruments on bycatch and discards with AI adoption to make it more attractive to fishers; and (3) using data management technologies to create trust in sharing data.