The issue of processing data in Aquaculture due to the development of smart-farming implying the use of intelligence artificial and machine-learning, is raising legal and ethics concerns regarding privacy, use of data as an object of commercial and national strategy by Companies and States or the risk of error when it comes to « unfair algorithms ».
Most of the publication and debates on the subject tended to focus on data ownership and Intellectual Property rights questions. From a legal point of view however, data should not be covered by IP rights, it is not provided by the Law first, but also, information should not be close but open (I). Recognizing this, many authors (mostly from US and Europe) wrote about the importance of building strong Contracts, code of conducts and provisions anticipating those issues and pointing out the owner of data, the liability solution in case of breach of security, data licenses or data sharing and control. Unfortunately, those solutions do not take into account the contractual imbalance who exists between parties and especially vulnerable communities as smallholders in Aquaculture or more generally consumers. It appears then, that the question of ownership is not the most relevant and that instead, the question should focus on the use and access to the data (II): effectivity of the consent, fair use, combatting data black market.
The development of data protection law on personal data is a good news for privacy and security. The principles of limited purpose for the use of data, adequacy and relevance, quality, minimization, lawful basis data subject rights, appropriate measures against unauthorized or unlawful processing and accidental loss or destruction and the rules on transfer to third countries and third parties coming from the European Union and the GDPR are spreading to Asia.
However, it is only aim personal information thought, and most of the data in Aquaculture is non-personal information.
Besides, we have to think about ethics consideration when manual and automated data collection implies to collect sensitive data who could disrupt communities (data on forest rights, over plotting, child labor).