@incollection{LevinaMattern2023, author = {Levina, Olga and Mattern, Saskia}, title = {Ethical and Legal Analysis of Machine Learning Based Systems: A Scenario Analysis of a Food Recommender System}, series = {Recommender Systems: Legal and Ethical Issues}, booktitle = {Recommender Systems: Legal and Ethical Issues}, isbn = {978-3-031-34806-8}, doi = {10.1007/978-3-031-34804-4_9}, pages = {165 -- 185}, year = {2023}, abstract = {Laws are the reflection of the ethical and moral principles of the society. While the use of technology influences users' behavior in a pace that is affected by the technology introduction to the market, legal activities can be driven by the society as the results of such interactions. This scenario analysis- based research focuses on a classic but fictional food recommender system and the ethical issues that might occur from its usage. The recommender system is taken here as an example of machine learning-based systems (MLS) that can often be found in the individual, business and administrative applications. The research compares the existing legal solutions, with the focus on the GDPR legislation, and the discovered ethical issues. The ethical analysis is led along the ALTAI principles suggested by the European Commission, the common good approach as well as the general principles constituted in human rights. While the GDPR-based analysis showed that this data- and privacy-based legislation addressed most of the identified ethical issues, questions related to the common good approach in the context of environment and mobility that arise due to the wide spectrum of the MLS usage require further legal discussion. The application of the two approaches shows that conducting the ethical and legal analysis is beneficial for both the designers of such MLS as well as the legal actors. The findings can enhance the design and functions of a user-facing MLS as well as influence or validate legal activities.}, language = {en} }