Advances in Bias and Fairness in Information Retrieval

Chapter 1 A Study on Accuracy, Miscalibration, and Popularity Bias in Recommendations

Chapter 2 Measuring Bias in Multimodal Models: Multimodal Composite Association Score

Chapter 4 Utilizing Implicit Feedback for User Mainstreaminess Evaluation and Bias Detection in Recommender Systems

Chapter 5 Preserving Utility in Fair Top- k Ranking with Intersectional Bias

Chapter 6 Mitigating Position Bias in Hotels Recommender Systems

Chapter 7 Improving Recommender System Diversity with Variational Autoencoders

Chapter 8 Addressing Biases in the Texts Using an End-to-End Pipeline Approach

Chapter 9 Bootless Application of Greedy Re-ranking Algorithms in Fair Neural Team Formation

Chapter 10 How Do You Feel? Information Retrieval in Psychotherapy and Fair Ranking Assessment

Chapter 11 Understanding Search Behavior Bias in Wikipedia

Chapter 12 Do You MIND? Reflections on the MIND Dataset for Research on Diversity in News Recommendations

Chapter 13 Detecting and Measuring Social Bias of Arabic Generative Models in the Context of Search and Recommendation

Chapter 14 What Are We Missing in Algorithmic Fairness? Discussing Open Challenges for Fairness Analysis in User Profiling with Graph Neural Networks