Recommender Systems : : Legal and Ethical Issues.

• Superior document: The International Library of Ethics, Law and Technology Series ; v.40
• Author: Genovesi, Sergio.
• TeilnehmendeR: Kaesling, Katharina.; Robbins, Scott.
• Place / Publishing House: Cham : : Springer International Publishing AG,, 2023.; ©2023.
• Year of Publication: 2023
• Edition: 1st ed.
• Language: English
• Series: The International Library of Ethics, Law and Technology Series
• Physical Description: 1 online resource (220 pages)

Table of Contents

• 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.