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

AvLudovico Boratto,Stefano Faralli

Häftad, Engelska, 2021

Del 1418 i serien Communications in Computer and Information Science

559 kr

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2021-06-25
Mått:155 x 235 x 11 mm
Vikt:289 g
Format:Häftad
Språk:Engelska
Serie:Communications in Computer and Information Science
Antal sidor:171
Förlag:Springer Nature Switzerland AG
ISBN:9783030788179

Utforska kategorier

Databaser inom Data och IT

Innehållsförteckning

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.