Nordic Machine Intelligence, vol. 02, pp. 1–3, 2022 https://doi.org/10.5617/nmi.9657 FishAI: Sustainable Commercial Fishing Tor-Arne Schmidt Nordmo 1 , Ove Kvalsvik 2 , Svein Ove Kvalsund 2 , Birte Hansen 3 , Dag Johansen 1 , Håvard Dagenborg Johansen 1 , Michael A. Riegler 1, 4 1. UiT The Arctic University of Norway, Norway 2. Vekstlandet, Norway 3. NORA—Norwegian Artificial Intelligence Research Consortium, Norway 4. SimulaMet, Norway Abstract FishAI: Sustainable Commercial Fishing is the second chal- lenge at the Nordic AI Meet following the successful MedAI, which had a focus on medical image segmentation and trans- parency in machine learning (ML)-based systems. FishAI fo- cuses on a new domain, namely, commercial fishing and how to make it more sustainable with the help of machine learning. A range of public available datasets is used to tackle three spe- cific tasks. The first one is to predict fishing coordinates to optimize catching of specific fish, the second one is to create a report that can be used by experienced fishermen, and the third task is to make a sustainable fishing plan that provides a route for a week. The second and third task require to some extend explainable and interpretable models that can provide explanations. A development dataset is provided and all meth- ods will be tested on a concealed test dataset and assessed by an expert jury. Keywords: artificial intelligence; machine learning; trans- parency; fishing; automatic reporting Introduction With a őshing zone spanning 2.1 million square meters, Norway is considered Europe’s largest őshing and aqua- culture nation. Every year, commercial vessels catch ősh with a total value of around 20 billion NOK from the Nor- wegian őshing zone. The overall migration patterns of the major ősh species are relatively predictable and common knowledge. A ősherman knows, for example, that the mackerel season starts in mid-September and plans accordingly. On a daily basis, however, ősh populations can move over large distances, and with the main decision-making tool being the captain´s experience and intuition, boats often search for days or even weeks before making a catch. The number of boats is not negligible; there are currently around 1,100 Norwegian vessels over 11 meters involved. It is estimated that each vessel burns around 2,000ś2,500 liters of fuel per day, which translates to approximately 5 million kg CO2-equivalents per day. Although the őshing ŕeet over time has shown an impressive ability to renew itself, the core operation of searching and catching ősh clearly has room for improvements in a sustainability context. Speciőcally, a more energy-efcient commercial őshing practice and operation should be a goal. In other words, there are great environmental beneőts and opportunities in optimizing commercial őshing activities by reducing unnecessary transport distances. With the recent release of catch data made available by the Norwegian Directorate of Fisheries, a signiőcant potential of applying artiőcial intelligence opened up, which we want to explore with this challenge. Dataset Details We provide the participants with a collection of four publicly available datasets: a catch note dataset, a temperature dataset, a salinity dataset, and a moon phase dataset. All datasets can be used in all tasks and can be downloaded via: https://tinyurl.com/54w5bvxa. For the GPS coordinates predictions, the catch notes dataset is the ground truth. Participants are also encouraged to use other data sources if they are public available. In the following we provide a more detailed description of each dataset and what the participants can expect for the evaluation of their results. Catch Notes Dataset The catch notes data contains catch notes collected by the Norwegian Fishing Directorate from 2000 to today for vessels larger than 15 meters. The notes consist of information about the catch that is manually logged during landing, e.g., when it was caught, where it was © 2022 Author(s). This is an open access article licensed under the Creative Commons Attribution License 4.0. (http://creativecommons.org/licenses/by/4.0/).