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001450640 001__ 1450640
001450640 003__ OCoLC
001450640 005__ 20230310004536.0
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001450640 019__ $$a1348635137
001450640 020__ $$a9783031093678$$q(electronic bk.)
001450640 020__ $$a3031093674$$q(electronic bk.)
001450640 020__ $$z3031093666
001450640 020__ $$z9783031093661
001450640 0247_ $$a10.1007/978-3-031-09367-8$$2doi
001450640 035__ $$aSP(OCoLC)1348693624
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001450640 049__ $$aISEA
001450640 050_4 $$aQA76.9.B45
001450640 08204 $$a005.7$$223/eng/20221025
001450640 1001_ $$aSapienza, Salvatore,$$eauthor.
001450640 24510 $$aBig data, algorithms and food safety :$$ba legal and ethical approach to data ownership and data governance /$$cSalvatore Sapienza.
001450640 264_1 $$aCham :$$bSpringer,$$c[2022]
001450640 264_4 $$c©2022
001450640 300__ $$a1 online resource (xiv, 216 pages) :$$billustrations.
001450640 336__ $$atext$$btxt$$2rdacontent
001450640 337__ $$acomputer$$bc$$2rdamedia
001450640 338__ $$aonline resource$$bcr$$2rdacarrier
001450640 4901_ $$aLaw, governance and technology series ;$$vvolume 52
001450640 504__ $$aIncludes bibliographical references.
001450640 5050_ $$aChapter 1:Food, Big Data, Artificial Intelligence -- Chapter 2:Data Ownership in Food-related Information -- Chapter 3:Food Consumption Data Protection -- Chapter 4:Current and Foreseeable Trends in Food Safety Data Governance -- Chapter 5: The P-SAFETY Model: a Unifying Ethical Approach -- Chapter 6: Conclusion: a Responsible Food Innovation.
001450640 506__ $$aAccess limited to authorized users.
001450640 520__ $$aThis book identifies the principles that should be applied when processing Big Data in the context of food safety risk assessments. Food safety is a critical goal in the protection of individuals right to health and the flourishing of the food and feed market. Big Data is fostering new applications capable of enhancing the accuracy of food safety risk assessments. An extraordinary amount of information is analysed to detect the existence or predict the likelihood of future risks, also by means of machine learning algorithms. Big Data and novel analysis techniques are topics of growing interest for food safety agencies, including the European Food Safety Authority (EFSA). This wealth of information brings with it both opportunities and risks concerning the extraction of meaningful inferences from data. However, conflicting interests and tensions among the parties involved are hindering efforts to find shared methods for steering the processing of Big Data in a sound, transparent and trustworthy way. While consumers call for more transparency, food business operators tend to be reluctant to share informational assets. This has resulted in a considerable lack of trust in the EU food safety system. A recent legislative reform, supported by new legal cases, aims to restore confidence in the risk analysis system by reshaping the meaning of data ownership in this domain. While this regulatory approach is being established, breakthrough analytics techniques are encouraging thinking about the next steps in managing food safety data in the age of machine learning. The book focuses on two core topics data ownership and data governance by evaluating how the regulatory framework addresses the challenges raised by Big Data and its analysis in an applied, significant, and overlooked domain. To do so, it adopts an interdisciplinary approach that considers both the technological advances and the policy tools adopted in the European Union, while also assuming an ethical perspective when exploring potential solutions. The conclusion puts forward a proposal: an ethical blueprint for identifying the principles Security, Accountability, Fairness, Explainability, Transparency and Privacy to be observed when processing Big Data for food safety purposes, including by means of machine learning. Possible implementations are then discussed, also in connection with two recent legislative proposals, namely the Data Governance Act and the Artificial Intelligence Act.
001450640 588__ $$aDescription based on print version record.
001450640 650_0 $$aBig data.
001450640 650_0 $$aAlgorithms.
001450640 650_0 $$aFood$$xSafety measures.
001450640 650_0 $$aData sovereignty.
001450640 650_0 $$aData protection.
001450640 655_0 $$aElectronic books.
001450640 77608 $$iPrint version:$$aSAPIENZA, SALVATORE.$$tBIG DATA, ALGORITHMS AND FOOD SAFETY.$$d[Place of publication not identified] : SPRINGER, 2022$$z3031093666$$w(OCoLC)1322366609
001450640 830_0 $$aLaw, governance and technology series ;$$vv. 52.
001450640 852__ $$bebk
001450640 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-09367-8$$zOnline Access$$91397441.1
001450640 909CO $$ooai:library.usi.edu:1450640$$pGLOBAL_SET
001450640 980__ $$aBIB
001450640 980__ $$aEBOOK
001450640 982__ $$aEbook
001450640 983__ $$aOnline
001450640 994__ $$a92$$bISE