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ORCIDBIAS 2024: Washington, DC, USA
- Alejandro Bellogín
, Ludovico Boratto, Styliani Kleanthous, Elisabeth Lex, Francesca Maridina Malloci, Mirko Marras:
Advances in Bias and Fairness in Information Retrieval - 5th International Workshop, BIAS 2024, Washington, DC, USA, July 18, 2024, Revised Selected Papers. Communications in Computer and Information Science 2227, Springer 2025, ISBN 978-3-031-71974-5 - Jonathan H. Rystrøm:
An Offer You Cannot Refuse? Trends in the Coercive Impact of Amazon Book Recommendations. 1-15 - Shichao Ma:
Retention Induced Biases in a Recommendation System with Heterogeneous Users. 16-31 - Takeshi Onishi, James Caverlee:
Political Bias of Large Language Models in Few-Shot News Summarization. 32-45 - Mohammad Mahdi Mohajer, Alvine Boaye Belle, Nima Shiri Harzevili, Junjie Wang, Hadi Hemmati, Song Wang, Zhen Ming (Jack) Jiang:
Fairness Analysis of Machine Learning-Based Code Reviewer Recommendation. 46-63 - Nathan Bartley, Keith Burghardt, Kristina Lerman:
Bias Reduction in Social Networks Through Agent-Based Simulations. 64-77 - Roonak Moasses, Delaram Rajaei, Hamed Loghmani, Mahdis Saeedi, Hossein Fani:
[inline-graphic not available: see fulltext] : Mitigating Gender Bias in Neural Team Recommendation via Female-Advocate Loss Regularization. 78-90 - Gustavo Escobedo, Christian Ganhör, Stefan Brandl, Mirjam Augstein, Markus Schedl:
Simultaneous Unlearning of Multiple Protected User Attributes From Variational Autoencoder Recommenders Using Adversarial Training. 91-102
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