Advances in Bias and Fairness in Information Retrieval
Springer International Publishing (Verlag)
978-3-031-37248-3 (ISBN)
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.
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.
| Erscheinungsdatum | 17.07.2023 |
|---|---|
| Reihe/Serie | Communications in Computer and Information Science |
| Zusatzinfo | X, 177 p. 43 illus., 37 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 296 g |
| Themenwelt | Informatik ► Weitere Themen ► Hardware |
| Schlagworte | Artificial Intelligence • Bias • Collaborative Filtering • Computer Networks • Computer systems • data and algorithmic bias • Electronic Commerce • Human-Computer Interaction (HCI) • Information Retrieval • machine learning • Network Protocols • personalizations • recommendation algorithms • Recommender Systems • Search Engines • User Interfaces • Web Search |
| ISBN-10 | 3-031-37248-4 / 3031372484 |
| ISBN-13 | 978-3-031-37248-3 / 9783031372483 |
| Zustand | Neuware |
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