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Advances in Bias and Fairness in Information Retrieval: Second International Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2021, Lucca, Italy, April 1, 2021, Proceedings de Ludovico Boratto

Advances in Bias and Fairness in Information Retrieval: Second International Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2021, Lucca, Italy, April 1, 2021, Proceedings: Communications in Computer and Information Science, cartea 1418

Editat de Ludovico Boratto, Stefano Faralli, Mirko Marras, Giovanni Stilo
en Limba Engleză Paperback – 25 iun 2021
This book constitutes refereed proceedings of the Second International Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2021, held in April, 2021. Due to the COVID-19 pandemic BIAS 2021 was held virtually. 

The 11 full papers and 3 short papers were carefully reviewed and selected from 37 submissions. The papers cover topics that go from search and recommendation in online dating, education, and social media, over the impact of gender bias in word embeddings, to tools that allow to explore bias and fairnesson the Web. 
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Specificații

ISBN-13: 9783030788179
ISBN-10: 3030788172
Pagini: 171
Ilustrații: X, 171 p. 40 illus., 34 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.27 kg
Ediția:1st ed. 2021
Editura: Springer International Publishing
Colecția Springer
Seria Communications in Computer and Information Science

Locul publicării:Cham, Switzerland

Cuprins

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.