Ethical Perspectives on Big Data in Agri-food: Ownership and Governance for Safety

Sapienza, Salvatore (2021) Ethical Perspectives on Big Data in Agri-food: Ownership and Governance for Safety, [Dissertation thesis], Alma Mater Studiorum Università di Bologna. Dottorato di ricerca in Law, science and technology, 33 Ciclo.
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Abstract

Big data are reshaping the way we interact with technology, thus fostering new applications to increase the safety-assessment of foods. An extraordinary amount of information is analysed using machine learning approaches aimed at detecting the existence or predicting the likelihood of future risks. Food business operators have to share the results of these analyses when applying to place on the market regulated products, whereas agri-food safety agencies (including the European Food Safety Authority) are exploring new avenues to increase the accuracy of their evaluations by processing Big data. Such an informational endowment brings with it opportunities and risks correlated to the extraction of meaningful inferences from data. However, conflicting interests and tensions among the involved entities - the industry, food safety agencies, and consumers - hinder the finding of shared methods to steer the processing of Big data in a sound, transparent and trustworthy way. A recent reform in the EU sectoral legislation, the lack of trust and the presence of a considerable number of stakeholders highlight the need of ethical contributions aimed at steering the development and the deployment of Big data applications. Moreover, Artificial Intelligence guidelines and charters published by European Union institutions and Member States have to be discussed in light of applied contexts, including the one at stake. This thesis aims to contribute to these goals by discussing what principles should be put forward when processing Big data in the context of agri-food safety-risk assessment. The research focuses on two interviewed topics - data ownership and data governance - by evaluating how the regulatory framework addresses the challenges raised by Big data analysis in these domains. The outcome of the project is a tentative Roadmap aimed to identify the principles to be observed when processing Big data in this domain and their possible implementations.

Abstract
Tipologia del documento
Tesi di dottorato
Autore
Sapienza, Salvatore
Supervisore
Co-supervisore
Dottorato di ricerca
Ciclo
33
Coordinatore
Settore disciplinare
Settore concorsuale
Parole chiave
AI - Big Data - Machine Learning - Food Safety - Data Ethics - Data Governance - Data Ownership
URN:NBN
Data di discussione
26 Marzo 2021
URI

Altri metadati

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