02046nam0 22004693i 450 VAN021634220230606023702.176N978303027103920210924d2019 |0itac50 baengCH|||| |||||Light SciencePhysics and the Visual ArtsThomas D. Rossing, Christopher J. Chiaverina2. edChamSpringer2019xi, 490 p.ill.24 cmVAN0216348Light Science : Physics and the Visual Arts187095978-XXOptics, electromagnetic theory [MSC 2020]VANC022356MF00A79 (77-XX)Physics [MSC 2020]VANC023182MF83-XXRelativity and gravitational theory [MSC 2020]VANC023243MF81V80Quantum optics [MSC 2020]VANC023592MF78A60Lasers, masers, optical bistability, nonlinear optics [MSC 2020]VANC029030MFColor perceptionKW:KInterference of light wavesKW:KLight explainedKW:KLight in the visual artsKW:KOptical phenomenaKW:KPhysiology of the eyeKW:KPrisms and refractionKW:KWhat is lightKW:KCHChamVANL001889RossingThomas D.VANV02677461179ChiaverinaChristopher J.VANV184781837412Springer <editore>VANV108073650ITSOL20240614RICAhttp://doi.org/10.1007/978-3-030-27103-9E-book – Accesso al full-text attraverso riconoscimento IP di Ateneo, proxy e/o ShibbolethBIBLIOTECA DEL DIPARTIMENTO DI MATEMATICA E FISICAIT-CE0120VAN08NVAN0216342BIBLIOTECA DEL DIPARTIMENTO DI MATEMATICA E FISICA08CONS e-book 3942 08eMF3942 20210924 Light Science : Physics and the Visual Arts1870959UNICAMPANIA05160nam 22006735 450 991061927670332120230810175633.09783031093678(electronic bk.)978303109366110.1007/978-3-031-09367-8(MiAaPQ)EBC7120435(Au-PeEL)EBL7120435(CKB)25181489400041(DE-He213)978-3-031-09367-8(PPN)26585752X(EXLCZ)992518148940004120221020d2022 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierBig Data, Algorithms and Food Safety A Legal and Ethical Approach to Data Ownership and Data Governance /by Salvatore Sapienza1st ed. 2022.Cham :Springer International Publishing :Imprint: Springer,2022.1 online resource (225 pages)Law, Governance and Technology Series,2352-1910 ;52Print version: Sapienza, Salvatore Big Data, Algorithms and Food Safety Cham : Springer International Publishing AG,c2022 9783031093661 Includes bibliographical references.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.Law, Governance and Technology Series,2352-1910 ;52Information technologyLaw and legislationMass mediaLaw and legislationArtificial intelligenceBig dataFoodSafety measuresIT Law, Media Law, Intellectual PropertyArtificial IntelligenceBig DataFood SafetyInformation technologyLaw and legislation.Mass mediaLaw and legislation.Artificial intelligence.Big data.FoodSafety measures.IT Law, Media Law, Intellectual Property.Artificial Intelligence.Big Data.Food Safety.005.7005.7Sapienza Salvatore1262886MiAaPQMiAaPQMiAaPQ9910619276703321Big Data, Algorithms and Food Safety2954919UNINA