Recommender Systems : : Legal and Ethical Issues.
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Superior document: | The International Library of Ethics, Law and Technology Series ; v.40 |
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TeilnehmendeR: | |
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
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Online Access: | |
Physical Description: | 1 online resource (220 pages) |
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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.