OPC4 - results/titledata

Intro -- Contents -- Chapter 1: Introduction: Understanding and Regulating AI-Powered Recommender Systems -- References -- Part I: Fairness and Transparency -- Chapter 2: Recommender Systems and Discrimination -- 2.1 Introduction -- 2.2 Reasons for Discriminating Recommendations -- 2.2.1 Lack of Diversity in Training Data -- 2.2.2 (Unconscious) Bias in Training Data -- 2.2.3 Modelling Algorithm -- 2.2.4 Interim Conclusion and Thoughts -- 2.3 Legal Frame -- 2.3.1 Agreement - Data Protection Law -- 2.3.2 Information - Unfair Competition Law -- 2.3.3 General Anti-discrimination Law -- 2.3.4 Interim Conclusion -- 2.4 Outlook -- 2.4.1 Extreme Solutions -- 2.4.2 Further Development of the Information Approach -- 2.4.3 Monitoring and Audit Obligations -- 2.4.4 Interim Conclusion and Thoughts -- 2.5 Conclusions -- References -- Chapter 3: From Algorithmic Transparency to Algorithmic Choice: European Perspectives on Recommender Systems and Platform Regulation -- 3.1 Introduction -- 3.2 Recommender Governance in the EU Platform Economy -- 3.2.1 Mapping the Regulatory Landscape -- 3.2.2 Layers of Terminology in EU Law: "Rankings" and "Recommender Systems" -- 3.3 Five Axes of Algorithmic Transparency: A Comparative Analysis -- 3.3.1 Purpose of Transparency -- 3.3.2 Audiences of Disclosure -- 3.3.3 Addressees of the Duty to Disclose -- 3.3.4 Content of the Disclosure -- 3.3.5 Modalities of Disclosure -- 3.4 The Digital Services Act: From Algorithmic Transparency to Algorithmic Choice? -- 3.4.1 Extension of Transparency Rules -- 3.4.2 User Control Over Ranking Criteria -- 3.5 Third Party Recommender Systems: Towards a Market for "RecommenderTech" -- 3.6 Conclusion -- References -- Chapter 4: Black Hole Instead of Black Box?: The Double Opaqueness of Recommender Systems on Gaming Platforms and Its Legal Implications -- 4.1 Introduction.