國立虎尾科技大學 |

Big Data, Algorithms and Food Safety = A Legal and Ethical Approach to Data Ownership and Data Governance /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Big Data, Algorithms and Food Safety/ by Salvatore Sapienza.
Reminder of title:	A Legal and Ethical Approach to Data Ownership and Data Governance /
Author:	Sapienza, Salvatore.
Description:	XIV, 216 p. 1 illus.online resource. :
Contained By:	Springer Nature eBook
Subject:	Information technology—Law and legislation. -
Online resource:	https://doi.org/10.1007/978-3-031-09367-8
ISBN:	9783031093678

Big Data, Algorithms and Food Safety = A Legal and Ethical Approach to Data Ownership and Data Governance /
Sapienza, Salvatore.

Big Data, Algorithms and Food SafetyA Legal and Ethical Approach to Data Ownership and Data Governance /[electronic resource] :by Salvatore Sapienza. - 1st ed. 2022. - XIV, 216 p. 1 illus.online resource. - Law, Governance and Technology Series,522352-1910 ;. - Law, Governance and Technology Series,21.

Chapter 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.

This 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.

ISBN: 9783031093678

Standard No.: 10.1007/978-3-031-09367-8doiSubjects--Topical Terms:

1388059
Information technology—Law and legislation.

LC Class. No.: K4240-4343

Dewey Class. No.: 343.099

Big Data, Algorithms and Food Safety = A Legal and Ethical Approach to Data Ownership and Data Governance /
LDR:04301nam a22004095i 4500 001 1084480
003 DE-He213
005 20221020085937.0
007 cr nn 008mamaa
008 221228s2022 sz | s |||| 0|eng d
020 $a 9783031093678 $9 978-3-031-09367-8
024 7 $a 10.1007/978-3-031-09367-8 $2 doi
035 $a 978-3-031-09367-8
050 4 $a K4240-4343
072 7 $a LNJ $2 bicssc
072 7 $a LAW000000 $2 bisacsh
072 7 $a LNJ $2 thema
082 0 4 $a 343.099 $2 23
100 1 $a Sapienza, Salvatore. $e author. $0 (orcid)0000-0002-5429-5217 $1 https://orcid.org/0000-0002-5429-5217 $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1390769
245 1 0 $a Big Data, Algorithms and Food Safety $h [electronic resource] : $b A Legal and Ethical Approach to Data Ownership and Data Governance / $c by Salvatore Sapienza.
250 $a 1st ed. 2022.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a XIV, 216 p. 1 illus. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Law, Governance and Technology Series, $x 2352-1910 ; $v 52
505 0 $a Chapter 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.
520 $a This 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.
650 0 $a Information technology—Law and legislation. $3 1388059
650 0 $a Mass media—Law and legislation. $3 1388060
650 0 $a Artificial intelligence. $3 559380
650 0 $a Big data. $3 981821
650 0 $a Food—Safety measures. $3 1366579
650 1 4 $a IT Law, Media Law, Intellectual Property. $3 1209939
650 2 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Big Data. $3 1017136
650 2 4 $a Food Safety. $3 1058643
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783031093661
776 0 8 $i Printed edition: $z 9783031093685
776 0 8 $i Printed edition: $z 9783031093692
830 0 $a Law, Governance and Technology Series, $x 2352-1902 ; $v 21 $3 1254645
856 4 0 $u https://doi.org/10.1007/978-3-031-09367-8
912 $a ZDB-2-LCR
912 $a ZDB-2-SXLC
950 $a Law and Criminology (SpringerNature-41177)
950 $a Law and Criminology (R0) (SpringerNature-43727)