Books+ Search Results
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
Title
Advances in Bias and Fairness in Information Retrieval [electronic resource] : Second International Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2021, Lucca, Italy, April 1, 2021, Proceedings / edited by Ludovico Boratto, Stefano Faralli, Mirko Marras, Giovanni Stilo.
ISBN
9783030788186
Edition
1st ed. 2021.
Publication
Cham : Springer International Publishing : Imprint: Springer, 2021.
Physical Description
X, 171 p. 40 illus., 34 illus. in color.
Local Notes
Access is available to the Yale community.
Access and use
Access restricted by licensing agreement.
Summary
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. .
Variant and related titles
Springer ENIN.
Other formats
Printed edition:
Printed edition:
Format
Books / Online
Language
English
Added to Catalog
July 12, 2021
Series
Communications in Computer and Information Science, 1418
Contents
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.
Subjects
Also listed under
SpringerLink (Online service)
Bookmark As
Citation
