How emerging data technologies can increase trust and transparency in fisheries

被引:48
作者
Probst, Wolfgang Nikolaus [1 ]
机构
[1] Johann Heinrich von Thunen Inst Sea Fisheries, Herwigstr 31, D-27572 Bremerhaven, Germany
关键词
artificial intelligence; blockchain; data mining; enforcement; supply chain; traceability; SMARTPHONE ADDICTION; MACHINE; CLASSIFICATION; INTERNET; SUPPORT; SEA;
D O I
10.1093/icesjms/fsz036
中图分类号
S9 [水产、渔业];
学科分类号
0908 ;
摘要
The ubiquitous spread of digital networks has created techniques which can organize, store, and analyse large data volumes in an automized and self-administered manner in real time. These technologies will have profound impacts on policy, administration, economy, trade, society, and science. This article sketches how three digital data technologies, namely the blockchain, data mining, and artificial intelligence could impact commercial fisheries including producers, wholesalers, retailers, consumers, management authorities, and scientist. Each of these three technologies is currently experiencing an enormous boost in technological development and real-world implementation and is predicted to increasingly affect many aspects of fisheries and seafood trade. As any economic sector acting on global scales, fishing and seafood production are often challenged with a lack of trust along various steps of the production process and supply chain. Consumers are often not well informed on the origin and production methods of their product, management authorities can only partly control fishing and trading activities and producers can be challenged by low market prices and competition with peers. The emerging data technologies can improve the trust among agents within the fisheries sector by increasing transparency and availability of information from net to plate.
引用
收藏
页码:1286 / 1294
页数:9
相关论文
共 58 条
[11]   Blockchains and Smart Contracts for the Internet of Things [J].
Christidis, Konstantinos ;
Devetsikiotis, Michael .
IEEE ACCESS, 2016, 4 :2292-2303
[12]  
Dlodlo N., 2015, P INT C EM TRENDS NE
[13]  
Dorner H., 2018, Ethics in Science and Environmental Politics (ESEP), V18, P15, DOI 10.3354/esep00183
[14]   New Approaches to Marine Conservation Through the Scaling Up of Ecological Data [J].
Edgar, Graham J. ;
Bates, Amanda E. ;
Bird, Tomas J. ;
Jones, Alun H. ;
Kininmonth, Stuart ;
Stuart-Smith, Rick D. ;
Webb, Thomas J. .
ANNUAL REVIEW OF MARINE SCIENCE, VOL 8, 2016, 8 :435-+
[15]   Probabilistic machine learning and artificial intelligence [J].
Ghahramani, Zoubin .
NATURE, 2015, 521 (7553) :452-459
[16]   The emergence and effectiveness of the Marine Stewardship Council [J].
Gulbrandsen, Lars H. .
MARINE POLICY, 2009, 33 (04) :654-660
[17]   On bycatches [J].
Hall, MA .
REVIEWS IN FISH BIOLOGY AND FISHERIES, 1996, 6 (03) :319-352
[18]  
Haskell WB, 2014, AAAI CONF ARTIF INTE, P2978
[19]  
Hastie T., 2009, ELEMENTS STAT LEARNI, P605, DOI 10.1007/978-0-387-84858-716
[20]   Smartphone use and smartphone addiction among young people in Switzerland [J].
Haug, Severin ;
Castro, Raquel Paz ;
Kwon, Min ;
Filler, Andreas ;
Kowatsch, Tobias ;
Schaub, Michael P. .
JOURNAL OF BEHAVIORAL ADDICTIONS, 2015, 4 (04) :299-307