Advances in Bias and Fairness in Information Retrieval: Second International Workshop on Algorithmic Bias in Search and Recommendation, Bias 2021, Luc

ISBN-13: 9783030788179 / Angielski / Miękka / 2021 / 171 str.

Ludovico Boratto; Stefano Faralli; Mirko Marras

Spis treści

Towards Fairness-Aware Ranking by Defining Latent Groups Using Inferred Features.- Media Bias Everywhere? A Vision for Dealing with the Manipulation of Public Opinion.- Users' Perception of Search-Engine Biases and Satisfaction.- Preliminary Experiments to Examine the Stability of Bias-Aware Techniques.- Detecting Race and Gender Bias in Visual Representation of AI on Web Search Engines.- Equality of Opportunity in Ranking: A Fair-Distributive Model.- Incentives for Item Duplication under Fair Ranking Policies.- Quantification of the Impact of Popularity Bias in Multi-Stakeholder and Time-Aware Environment.- When is a Recommendation Model Wrong? A Model-Agnostic Tree-Based Approach to Detecting Biases in Recommendations.- Evaluating Video Recommendation Bias on YouTube.- An Information-Theoretic Measure for Enabling Category Exemptions with an Application to Filter Bubbles.- Perception-Aware Bias Detection for Query Suggestions.- Crucial Challenges in Large-Scale Black Box Analyses.- New Performance Metrics for Offline Content-based TV Recommender Systems.