Advances in Bias and Fairness in Information Retrieval
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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
Lex, Elisabeth; Marras, Mirko; Bellogin, Alejandro; Kleanthous, Styliani; Boratto, Ludovico; Malloci, Francesca Maridina
Springer International Publishing AG
11/2024
103
Mole
9783031719745
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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.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Information Retrieval;Recommender Systems;Web Search;Bias;Data and Algorithmic Bias;Fairness;Discrimination;Social Effects;Transparency;Accountability
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.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
