Recommender Systems and Discrimination

Abstract

AbstractThe following article deals with the topic of discrimination “by” a recommender system. Several reasons can create discriminating recommendations, especially the lack of diversity in training data, bias in training data or errors in the underlying modelling algorithm. The legal frame is still not sufficient to nudge developers or users to effectively avoid those discriminations, especially data protection law as enshrined in the EU General Data Protection Regulation (GDPR) is not feasible to fight discrimination. The same applies for the EU Unfair Competition Law, that at least contains first considerations to allow an autonomous decision of the subjects involved to know about possible forms of discrimination. Furthermore, with the Digital Service Act (DSA) and the AI Act (AIA) there are first steps into a direction that can inter alia tackle the problem. Most effectively seems a combination of regular monitoring and audit obligations and the development of an information model, supported by information by legal design, that allows an autonomous decision of all individuals using a recommender system.