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Advances in Bias and Fairness in Information Retrieval: 5th International Workshop, BIAS 2024, Washington, DC, USA, July 18, 2024, Revised Selected Papers 2024 ed. [Mīkstie vāki]

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  • Formāts: Paperback / softback, 103 pages, height x width: 235x155 mm, 25 Illustrations, color; 5 Illustrations, black and white; IX, 103 p. 30 illus., 25 illus. in color., 1 Paperback / softback
  • Sērija : Communications in Computer and Information Science 2227
  • Izdošanas datums: 23-Oct-2024
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3031719743
  • ISBN-13: 9783031719745
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Advances in Bias and Fairness in Information Retrieval: 5th International Workshop, BIAS 2024, Washington, DC, USA, July 18, 2024, Revised Selected Papers 2024 ed.Palielināt
  • Formāts: Paperback / softback, 103 pages, height x width: 235x155 mm, 25 Illustrations, color; 5 Illustrations, black and white; IX, 103 p. 30 illus., 25 illus. in color., 1 Paperback / softback
  • Sērija : Communications in Computer and Information Science 2227
  • Izdošanas datums: 23-Oct-2024
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3031719743
  • ISBN-13: 9783031719745
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783031719745.html
Keywords:
This book constitutes the refereed proceedings of the 5th International Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2024, held in Washington, DC, USA, on July 18, 2024 in hybrid mode.





The 7 full papers included in this book were carefully reviewed and selected from 20 submissions. They are grouped into three thematic sessions, each focusing on distinct aspects of bias and fairness in information retrieval.
An Offer you Cannot Refuse? Trends in the Coercive Impact of Amazon Book
Recommendations.- Retention Induced Biases in a Recommendation System with
Heterogeneous Users.- Political Bias of Large Language Models in Few-shot
News Summarization.- Fairness Analysis of Machine Learning-Based Code
Reviewer Recommendation.- Bias Reduction in Social Networks through
Agent-Based Simulations.- vivaFemme: Mitigating Gender Bias in Neural Team
Recommendation via Female-Advocate Loss Regularization.- Simultaneous
Unlearning of Multiple Protected User Attributes From Variational Autoencoder
Recommenders Using Adversarial Training.