Recommender Systems: Legal and Ethical Issues

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...

Descripción completa

Detalles Bibliográficos
Autor principal: Genovesi, Sergio (-)
Otros Autores: Kaesling, Katharina, Robbins, Scott
Formato: Libro electrónico
Idioma:Inglés
Publicado: Cham : Springer International Publishing 2023.
Edición:1st ed. 2023.
Colección:The International Library of Ethics, Law and Technology, 40
Materias:
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009762665206719
Tabla de Contenidos:
  • 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 Ourselves to 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.