Recommender Systems: Legal and Ethical Issues

Recommender Systems: Legal and Ethical Issues

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This open access contributed volume examines the ethical and legal foundations of (future) policies on recommender systems and offers a transdisciplinary approach to tackle important issues related to their development, use and integration into online eco-systems. This volume scrutinizes the values...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Genovesi, Sergio (Editor), Kaesling, Katharina (Editor), Robbins, Scott (Editor)
Format: Electronic eBook
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2023.
Edition:1st ed. 2023.
Series:The International Library of Ethics, Law and Technology, 40
Subjects:
Technology > Moral and ethical aspects.
Ethics of Technology.
Online Access:Link to Metadata
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Table of Contents:
  • Chapter 1: Introduction: Understanding and Regulating Al-Powered Recommender systems
  • Part I: Fairness and Transparency
  • Chapter 2: Recommender Systems and Discrimination
  • Chapter 3: From Algoritmic Transparency to Algorithmic Choice: European Perspectives on Recommender Systems and Platform Regulation
  • Chapter 4: Black Hole instead of Black Box? - The Double Opaqueness of Recommender Systems on Gaming Platforms and its Legal Implications
  • Chapter 5: Digital Labor as a Structural Fairness Issue in Recommender Systems
  • 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
  • Chapter 7: Reasoning with Recommender Systems? Practical Reasoning, Digital Nudging, and Autonomy
  • Chapter 8: Recommending Ourselvesto Death: values in the age of algorithms
  • 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
  • Chapter 10: Factors influencing trust and use of recommendation AI: A case study of diet improvement AI in Japan
  • Chapter 11: Ethics of E-Learning Recommender Systems: Epistemic Positioning and Ideological Orientation.

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