The world's fish stocks are in decline and our increasing demand for seafood could also be one among the most drivers. But truth extent of the matter is tough to estimate, especially when fishing occurs within the high seas, which lie beyond national jurisdiction and are hard to watch .
Conservation planners face growing pressures to combat illegal, unregulated and unreported (IUU) fishing, the worth of which has been estimated at US$10-23.5 billion annually. This is a crucial cost for society as an entire , but also for the main high seas fishing countries like China and Taiwan that subsidize their fleets and may have low labour costs.
Artificial intelligence (AI) could address this global environmental concern—and satisfy the need of seafood retailers and consumers to know if what they're selling and eating is sustainable. Social scientists are beginning to think of ways that can bring AI, ecology and economics together —to design policies that target socially desirable outcomes such as preserving biodiversity values and returning the benefits of fishing to society.
Tom Petrocelli wrote an article on CMS Wire that summarized this experience nicely. He points out that chatbots need more structured inputs than people do; appear to be using trial and error rather than showing true understanding; are a transparent effort for companies to reduce cost; are less efficient than using a mobile app; and, the most pointed critique: chatbots yield the same results any search engine would but with more effort. But these experiences on your phone or on the web aren't truly virtual assistants. They are either voice command interfaces, in the case of your phone, or either web forms or search engines put into a different interface in the case of the web. Whatever you call it, the user experience isn't always great, and it causes us to enter into interactions with chatbots with some skepticism.
At a February meeting of HUMAINT, a European Commission-led initiative on human behaviour and machine intelligence, I discussed the ways AI can be used to help marine resource management.
Fisheries and conservation managers have put tons of effort in recent years in establishing spatial management tools like marine protected areas to assist fish stocks get over past over-exploitation. Fish biomass in no-take marine reserves are often on the average 670 percent greater than in unprotected areas.
And at the same time, these experiences should:
Even though they're protected, these areas aren't always resistant to IUU fishing. Poaching occurs and cannot be tracked easily. This can make it difficult to guage the effectiveness of the protected area during a rigorous scientific manner.
IUU fishing results in environmental, economic and social costs —namely declining fish stocks —and can lead to a loss of profit for those fishers who play by the rules. It can turn the industry against the regulatory authorities that impose these spatial restrictions, undermine public trust in fisheries management and conservation science.
Tracking fishing with AI:
Traditionally, observers have been employed, at high cost, to monitor fishing activity on board vessels. But in remote locations, such as the Arctic, it can be difficult to find observers.
AI tools have the potential to lower monitoring and operational fishing costs and improve efficiency in fisheries management. Examples include automatic review of video footage, monitoring vessel sailing patterns for IUU fishing and illegal at-sea transshipments (moving goods from one ship to another), compliance with catch limits and bycatch or discard regulations, and improving assessment of fish stocks.
AI tools can also help build trust among fishers, scientists and society through improved seafood traceability.
Image recognition using AI can help identify the dimensions of a vessel and its activity. It can help conservation managers understand who fishes for what in high sea where it's unclear who the fish belong to. It may also contribute to a better understanding of how commercially fished invasive species are spreading.
However, there are also potential risks. Some fear the data may be used for unintended purposes or that AI tools might replace manually performed tasks and make human labour obsolete, a big concern for small, coastal fishery-dependent communities.
The way forward:
The Global Fishing Watch platform, an independent organization that emerged through a collaboration between Google, SkyTruth (a digital mapping non-profit organization) and Oceana, is a superb example of how combining AI and satellite data can change our understanding of global fishing activity.
Global Fishing Watch shows vessel movement in near real-time. Its work goes beyond tracking vessel activity: the neural network (computer program) it uses can identify vessel size and engine power, the sort of fishing being done and therefore the gear used. The ambitious project goes as far as tracking human slavery and rights abuse, a well-known phenomenon in the fishing industry.
The developments in AI applications are impressive in recent years, allowing a far better understanding of fishing activity across the world . Further progress in making them more widely applicable has been limited partly by the prices involved for the industry. Concerns about the impact of digital surveillance on privacy interests also are a problem .
Despite all the progress in AI science and therefore the development of advanced algorithms that improve the standard and speed of data transmitted for ongoing fishing activities stumped , there is still little or no formalized integration of science, regulatory authorities and therefore the fishing industry.
Making the best use of what AI tools have to offer requires experts to transcend their disciplinary boundaries and actively collaborate —so they can provide value to ongoing management efforts to conserve biodiversity and build trust among seafood consumers.