Big Data, Algorithms and Food Safety: A Legal and Ethical Approach to Data Ownership and Data Governance
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 applica...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch Software E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cham
Springer International Publishing
2022
Imprint: Springer 2022 |
| Ausgabe: | 1st ed. 2022 |
| Schriftenreihe: | Law, Governance and Technology Series
52 |
| Schlagworte: | |
| Online-Zugang: | DE-634 DE-M382 DE-91 DE-19 DE-706 DE-29 URL des Erstveröffentlichers |
| Zusammenfassung: | 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 |
| Beschreibung: | 1 Online-Ressource (XIV, 216 Seiten 1 illus.) |
| ISBN: | 9783031093678 |
| DOI: | 10.1007/978-3-031-09367-8 |
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| 520 | 3 | |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. | |
| 520 | 3 | |a 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. | |
| 520 | 3 | |a 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 | |
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| author | Sapienza, Salvatore |
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| contents | 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 |
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| discipline | Rechtswissenschaft Informatik |
| discipline_str_mv | Rechtswissenschaft Informatik |
| doi_str_mv | 10.1007/978-3-031-09367-8 |
| edition | 1st ed. 2022 |
| format | Electronic Software eBook |
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| id | DE-604.BV048557826 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T20:59:09Z |
| indexdate | 2024-11-05T11:01:00Z |
| institution | BVB |
| isbn | 9783031093678 |
| language | English |
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| spelling | Sapienza, Salvatore Verfasser aut Big Data, Algorithms and Food Safety A Legal and Ethical Approach to Data Ownership and Data Governance by Salvatore Sapienza 1st ed. 2022 Cham Springer International Publishing 2022 Imprint: Springer 2022 1 Online-Ressource (XIV, 216 Seiten 1 illus.) txt rdacontent c rdamedia cr rdacarrier text file PDF rda Law, Governance and Technology Series 52 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 IT Law, Media Law, Intellectual Property Artificial Intelligence Big Data Food Safety Information technology-Law and legislation Mass media-Law and legislation Artificial intelligence Big data Food-Safety measures Erscheint auch als Druck-Ausgabe 9783031093661 Erscheint auch als Druck-Ausgabe 9783031093685 Erscheint auch als Druck-Ausgabe 9783031093692 Law, Governance and Technology Series 52 (DE-604)BV040363410 52 https://doi.org/10.1007/978-3-031-09367-8 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Sapienza, Salvatore Big Data, Algorithms and Food Safety A Legal and Ethical Approach to Data Ownership and Data Governance Law, Governance and Technology Series 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 IT Law, Media Law, Intellectual Property Artificial Intelligence Big Data Food Safety Information technology-Law and legislation Mass media-Law and legislation Artificial intelligence Big data Food-Safety measures |
| title | Big Data, Algorithms and Food Safety A Legal and Ethical Approach to Data Ownership and Data Governance |
| title_auth | Big Data, Algorithms and Food Safety A Legal and Ethical Approach to Data Ownership and Data Governance |
| title_exact_search | Big Data, Algorithms and Food Safety A Legal and Ethical Approach to Data Ownership and Data Governance |
| title_exact_search_txtP | Big Data, Algorithms and Food Safety A Legal and Ethical Approach to Data Ownership and Data Governance |
| title_full | Big Data, Algorithms and Food Safety A Legal and Ethical Approach to Data Ownership and Data Governance by Salvatore Sapienza |
| title_fullStr | Big Data, Algorithms and Food Safety A Legal and Ethical Approach to Data Ownership and Data Governance by Salvatore Sapienza |
| title_full_unstemmed | Big Data, Algorithms and Food Safety A Legal and Ethical Approach to Data Ownership and Data Governance by Salvatore Sapienza |
| title_short | Big Data, Algorithms and Food Safety |
| title_sort | big data algorithms and food safety a legal and ethical approach to data ownership and data governance |
| title_sub | A Legal and Ethical Approach to Data Ownership and Data Governance |
| topic | IT Law, Media Law, Intellectual Property Artificial Intelligence Big Data Food Safety Information technology-Law and legislation Mass media-Law and legislation Artificial intelligence Big data Food-Safety measures |
| topic_facet | IT Law, Media Law, Intellectual Property Artificial Intelligence Big Data Food Safety Information technology-Law and legislation Mass media-Law and legislation Artificial intelligence Big data Food-Safety measures |
| url | https://doi.org/10.1007/978-3-031-09367-8 |
| volume_link | (DE-604)BV040363410 |
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