Abstract of my PhD Research Project
Big Data is reshaping the way we interact with technology, thus fostering new applications to increase the safety-assessment of foods, a critical goal in the protection of individuals' right to health and the flourishing of the food and feed market.
An extraordinary amount of information, including real-time data available from multiple sources, is analysed using machine learning approaches aimed at detecting the existences or predicting the likelihood of future risks, thus reducing the inaccuracy of risk assessment. Food business operators share the results of these analyses when applying to place on the market certain products, whereas agri-food safety agencies (including the European Food Safety Authority, EFSA) 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 robust, transparent and trustworthy manner.
Taken together, the recent reform in the EU sectoral legislation, the lack of trust in the EU food safety system proved by the recent Fitness Check of the General Food Law 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. At the same time, general AI guidelines published by European Union institutions and agencies have to be discussed in light of applied contexts, including the one at stake.
This thesis aims to contribute to this goal 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 narrow and interviewed topics - data ownership and data governance - by evaluating how the regulatory framework addresses the challenges raised by Big Data in this domain. To do so, it adopts a cross-disciplinary research methodology that keeps into account both the technological advances and the policy tools adopted in the European Union, while assuming an ethical perspective when exploring potential solutions. 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.
