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3 Fairness of information access systems

  • Markus Schedl and Elisabeth Lex
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Personalized Human-Computer Interaction
This chapter is in the book Personalized Human-Computer Interaction

Abstract

Information access systems, such as search engines and recommender systems, affect many day-to-day decisions in modern societies by preselecting and ranking content users are exposed to on the web (e. g., products, music, movies or job advertisements). While they have undoubtedly improved users’ opportunities to find useful and relevant digital content, these systems and their underlying algorithms often exhibit several undesirable characteristics. Among them, harmful biases play a significant role and may even result in unfair or discriminating behavior of such systems. In this chapter, we give an introduction to the different kinds and sources of biases from various perspectives as well as their relation to algorithmic fairness considerations. We also review common computational metrics that formalize some of these biases. Subsequently, the major strategies to mitigate harmful biases are discussed and each is illustrated by presenting concrete state-of-the-art approaches from scientific literature. Finally, we round off by identifying open challenges in research on fair information access systems.

Abstract

Information access systems, such as search engines and recommender systems, affect many day-to-day decisions in modern societies by preselecting and ranking content users are exposed to on the web (e. g., products, music, movies or job advertisements). While they have undoubtedly improved users’ opportunities to find useful and relevant digital content, these systems and their underlying algorithms often exhibit several undesirable characteristics. Among them, harmful biases play a significant role and may even result in unfair or discriminating behavior of such systems. In this chapter, we give an introduction to the different kinds and sources of biases from various perspectives as well as their relation to algorithmic fairness considerations. We also review common computational metrics that formalize some of these biases. Subsequently, the major strategies to mitigate harmful biases are discussed and each is illustrated by presenting concrete state-of-the-art approaches from scientific literature. Finally, we round off by identifying open challenges in research on fair information access systems.

Chapters in this book

  1. Frontmatter I
  2. Introduction V
  3. Contents IX
  4. List of Contributing Authors XI
  5. Part I: Foundations of personalization
  6. 1 Theory-grounded user modeling for personalized HCI 1
  7. 2 User-centered recommender systems 33
  8. 3 Fairness of information access systems 59
  9. Part II: User input and feedback
  10. 4 Personalization and user modeling for interaction processes 81
  11. 5 How to use socio-emotional signals for adaptive training 99
  12. 6 Explanations and user control in recommender systems 129
  13. 7 Feedback loops and mutual reinforcement in personalized interaction 153
  14. Part III: Personalization purposes and goals
  15. 8 Personalizing the user interface for people with disabilities 175
  16. 9 Personalized persuasion for behavior change 205
  17. 10 Personalization approaches for remote collaborative interaction 237
  18. Part IV: Personalization domains
  19. 11 Listener awareness in music recommender systems: directions and current trends 279
  20. 12 Tourist trip recommendations – foundations, state of the art and challenges 313
  21. 13 Pictures as a tool for matching tourist preferences with destinations 337
  22. Index 355
https://doi.org/10.1515/9783110988567-003

Chapters in this book

  1. Frontmatter I
  2. Introduction V
  3. Contents IX
  4. List of Contributing Authors XI
  5. Part I: Foundations of personalization
  6. 1 Theory-grounded user modeling for personalized HCI 1
  7. 2 User-centered recommender systems 33
  8. 3 Fairness of information access systems 59
  9. Part II: User input and feedback
  10. 4 Personalization and user modeling for interaction processes 81
  11. 5 How to use socio-emotional signals for adaptive training 99
  12. 6 Explanations and user control in recommender systems 129
  13. 7 Feedback loops and mutual reinforcement in personalized interaction 153
  14. Part III: Personalization purposes and goals
  15. 8 Personalizing the user interface for people with disabilities 175
  16. 9 Personalized persuasion for behavior change 205
  17. 10 Personalization approaches for remote collaborative interaction 237
  18. Part IV: Personalization domains
  19. 11 Listener awareness in music recommender systems: directions and current trends 279
  20. 12 Tourist trip recommendations – foundations, state of the art and challenges 313
  21. 13 Pictures as a tool for matching tourist preferences with destinations 337
  22. Index 355
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