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Advances in Bias and Fairness in Information Retrieval 4th International Workshop, BIAS 2023, Dublin, Ireland, April 2, 2023, Revised Selected Papers
Title
Advances in Bias and Fairness in Information Retrieval [electronic resource] : 4th International Workshop, BIAS 2023, Dublin, Ireland, April 2, 2023, Revised Selected Papers / edited by Ludovico Boratto, Stefano Faralli, Mirko Marras, Giovanni Stilo.
ISBN
9783031372490
Edition
1st ed. 2023.
Publication
Cham : Springer Nature Switzerland : Imprint: Springer, 2023.
Physical Description
1 online resource (X, 177 p.) 43 illus., 37 illus. in color.
Local Notes
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Access restricted by licensing agreement.
Summary
This book constitutes the refereed proceedings of the 4th International Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2023, held in Dublin, Ireland, in April 2023. The 10 full papers and 4 short papers included in this book were carefully reviewed and selected from 36 submissions. The present recent research in the following topics: biases exploration and assessment; mitigation strategies against biases; biases in newly emerging domains of application, including healthcare, Wikipedia, and news, novel perspectives; and conceptualizations of biases in the context of generative models and graph neural networks.
Variant and related titles
Springer ENIN.
Other formats
Printed edition:
Printed edition:
Format
Books / Online
Language
English
Added to Catalog
July 26, 2023
Series
Communications in Computer and Information Science, 1840
Contents
A Study on Accuracy, Miscalibration, and Popularity Bias in Recommendations
Measuring Bias in Multimodal Models: Multimodal Composite Association Score
Evaluating Fairness Metrics
Utilizing Implicit Feedback for User Mainstreaminess Evaluation and Bias Detection in Recommender Systems
Preserving Utility in Fair Top-k Ranking with Intersectional Bias
Mitigating Position Bias in Hotels Recommender Systems
Improving Recommender System Diversity with Variational Autoencoders
Addressing Biases in the Texts using an End-to-End Pipeline Approach
Bootless Application of Greedy Re-ranking Algorithms in Fair Neural Team Formation
How do you feel? Information Retrieval in Psychotherapy and Fair Ranking Assessment
Understanding Search Behavior Bias in Wikipedia
Do you MIND? Reflections on the MIND dataset for research on diversity in news recommendations
Detecting and Measuring Social Bias of Arabic Generative Models in the Context of Search and Recommendation
What are we missing in algorithmic fairness? Discussing open challenges for fairness analysis in user profiling with Graph Neural Networks.
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