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.

4.2 The Black Box-Problem of AI Applications -- 4.2.1 Transparency and Explainability: An Introduction -- 4.2.2 Efficiency vs. Explainability of Machine Learning -- 4.2.3 Background of the Transparency Requirement -- 4.2.4 Criticism -- 4.2.5 In Terms of Recommender Systems -- 4.3 The Black Hole-Problem of Gaming Platforms -- 4.3.1 Types of Recommender Systems -- 4.3.1.1 Content-Based Filtering Methods -- 4.3.1.2 Collaborative Filtering Methods -- 4.3.1.3 Hybrid Filtering Methods -- 4.3.2 Black Hole Phenomenon -- 4.4 Legal Bases and Consequences -- 4.4.1 Legal Acts -- 4.4.2 Digital Services Act -- 4.4.2.1 Problem Description -- 4.4.2.2 Regulatory Content Related to Recommender Systems -- 4.4.3 Artificial Intelligence Act -- 4.4.3.1 Purpose of the Draft Act -- 4.4.3.2 Regulatory Content Related to Recommender Systems -- 4.4.4 Dealing with Legal Requirements -- 4.4.4.1 User-Oriented Transparency -- 4.4.4.2 Government Oversight -- 4.4.4.3 Combination of the Two Approaches with Additional Experts -- 4.5 Implementation of the Proposed Solutions -- 4.5.1 Standardization -- 4.5.2 Control Mechanisms -- 4.6 Conclusion -- References -- Chapter 5: Digital Labor as a Structural Fairness Issue in Recommender Systems -- 5.1 Introduction: Multisided (Un)Fairness in Recommender Systems -- 5.2 Digital Labor as a Structural Issue in Recommender Systems -- 5.3 Fairness Issues from Value Distribution to Work Conditions and Laborers' Awareness -- 5.4 Addressing the Problem -- 5.5 Conclusion -- References -- Part II: Manipulation and Personal Autonomy -- Chapter 6: Recommender Systems, Manipulation and Private Autonomy: How European Civil Law Regulates and Should Regulate Recommender Systems for the Benefit of Private Autonomy -- 6.1 Introduction -- 6.2 Autonomy and Influence in Private Law -- 6.3 Recommender Systems and Their Influence -- 6.4 Manipulation.

6.5 Recommender Systems and Manipulation -- 6.5.1 Recommendations in General -- 6.5.2 Labelled Recommendations -- 6.5.3 Unrelated Recommendations -- 6.5.3.1 In General -- 6.5.3.2 Targeted Recommendations -- 6.5.3.2.1 In General -- 6.5.3.2.2 Exploiting Emotions -- 6.5.3.2.3 Addressing Fears Through (Allegedly) Harm-Alleviating Offers -- 6.5.4 Interim Conclusion: Recommender Systems, Manipulation and Private Autonomy -- 6.6 Regulation Regarding Recommender Systems -- 6.6.1 Unexpected Recommendation Criteria -- 6.6.2 Targeted Recommendations Exploiting Emotions or Addressing Fears -- 6.6.3 Regulative Measures to Take Regarding Recommender Systems -- 6.7 Conclusion -- References -- Chapter 7: Reasoning with Recommender Systems? Practical Reasoning, Digital Nudging, and Autonomy -- 7.1 Introduction -- 7.2 Practical Reasoning, Choices, and Recommendations -- 7.3 Recommender Systems and Digital Nudging -- 7.4 Autonomy in Practical Reasoning with Recommender Systems -- 7.5 Conclusion -- References -- Chapter 8: Recommending Ourselves to Death: Values in the Age of Algorithms -- 8.1 Introduction -- 8.2 Distorting Forces -- 8.2.1 Past Evaluative Standards -- 8.2.2 Reducing to Computable Information -- 8.2.3 Proxies for 'Good' -- 8.2.4 Black Boxed -- 8.3 Changing Human Values -- 8.4 Same Problem with Humans? -- 8.5 Conclusion -- References -- Part III: Designing and Evaluating Recommender Systems -- Chapter 9: Ethical and Legal Analysis of Machine Learning Based Systems: A Scenario Analysis of a Food Recommender System -- 9.1 Introduction -- 9.2 An Example Application: FoodApp- the Application for Meal Delivery -- 9.3 Current Approaches to Ethical Analysis of Recommender Systems -- 9.4 Ethical Analysis -- 9.5 Legal Considerations -- 9.5.1 Data Protection Law -- 9.5.2 General Principles and Lawfulness of Processing Personal Data -- 9.5.3 Lawfulness.

9.5.4 Purpose Limitation and Access to Data -- 9.5.5 Data Minimization and Storage Limitation -- 9.5.6 Accuracy, Security and Impact Assessment -- 9.6 Results of the Combined Ethical and Legal Analysis Approach -- 9.7 Conclusion and Outlook -- References -- Chapter 10: Factors Influencing Trust and Use of Recommendation AI: A Case Study of Diet Improvement AI in Japan -- 10.1 Society 5.0 and Recommendation AI in Japan -- 10.2 Model for Ensuring Trustworthiness of AI Services -- 10.3 Components of a Trustworthy AI Model -- 10.3.1 AI Intervention -- 10.3.2 Data Management -- 10.3.3 Purpose of Use -- 10.4 Verification of Trustworthy AI Model: A Case Study of AI for Dietary Habit Improvement Recommendations -- 10.4.1 Subjects -- 10.4.2 Verification 1: AI Intervention -- 10.4.3 Verification 2: Data Management -- 10.4.4 Verification 3: Purpose of Use -- 10.4.5 Method -- 10.4.6 Results -- 10.4.6.1 AI Intervention -- 10.4.6.2 Data Management -- 10.4.6.3 Purpose of Use in Terms of Service Agreements -- 10.5 Necessary Elements for Trusted AI -- References -- Chapter 11: Ethics of E-Learning Recommender Systems: Epistemic Positioning and Ideological Orientation -- 11.1 Introduction -- 11.2 Methods of Recommender Systems -- 11.3 Recommender Systems in e-Learning -- 11.3.1 Filtering Techniques: What Implications on Social and Epistemic Open-Mindedness? -- 11.3.2 Model Selection: A Risk of Thinking Homogenization? -- 11.3.3 Assessment Methods: What Do They Value? -- 11.4 Problem Statement -- 11.5 Some Proposals -- 11.5.1 Knowledge-Based Recommendations -- 11.5.2 A Learner Model Coming from Cognitive and Educational Sciences -- 11.5.3 A Teaching Model Based on Empiric Analyses -- 11.5.4 Explainable Recommendations -- 11.6 Discussion and Conclusion -- References.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2023. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.