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Publication
Principle-based recommendations for big data and machine learning in food safety: the P-SAFETY model
Journal Contribution - Journal Article
Big data and Machine learning Techniques are reshaping the way in which food safety risk assessment is conducted. The
ongoing ‘datafication’ of food safety risk assessment activities and the progressive deployment of probabilistic models in
their practices requires a discussion on the advantages and disadvantages of these advances. In particular, the low level
of trust in EU food safety risk assessment framework highlighted in 2019 by an EU-funded survey could be exacerbated
by novel methods of analysis. The variety of processed data raises unique questions regarding the interplay of multiple
regulatory systems alongside food safety legislation. Provisions aiming to preserve the confidentiality of data and protect
personal information are juxtaposed to norms prescribing the public disclosure of scientific information. This research is
intended to provide guidance for data governance and data ownership issues that unfold from the ongoing transformation
of the technical and legal domains of food safety risk assessment. Following the reconstruction of technological advances
in data collection and analysis and the description of recent amendments to food safety legislation, emerging concerns are
discussed in light of the individual, collective and social implications of the deployment of cutting-edge Big Data collection
and analysis techniques. Then, a set of principle-based recommendations is proposed by adapting high-level principles
enshrined in institutional documents about Artificial Intelligence to the realm of food safety risk assessment. The proposed
set of recommendations adopts Safety, Accountability, Fairness, Explainability, Transparency as core principles (SAFETY),
whereas Privacy and data protection are used as a meta-principle.
Journal: AI and Society
ISSN: 1435-5655
Issue: 1
Volume: 36
Pages: 1 - 16
Publication year:2023
Accessibility:Open