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Advances in Bias and Fairne...
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.
Monografía
monografia Rebiun28807751 https://catalogo.rebiun.org/rebiun/record/Rebiun28807751 DE-He213 cr nn 008mamaa 210723s2021 gw | s |||| 0|eng d 9783030788186 978-3-030-78818-6 10.1007/978-3-030-78818-6 doi UPNA0541027 UR0516011 UPVA 997742286403706 UAM 991008097009704211 UCAR 991008232652404213 UR UT bicssc COM069000 bisacsh UT thema 005.7 23 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. 1st ed. 2021 Cham Springer International Publishing Imprint: Springer 2021. Cham Cham Springer International Publishing Imprint: Springer X, 171 p. 40 illus., 34 illus. in color. online resource. X, 171 p. 40 illus., 34 illus. in color. Text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Communications in Computer and Information Science 1865-0929 1418 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. 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. Computers. Information Systems and Communication Service. Boratto, Ludovico. editor. https://orcid.org/0000-0002-6053-3015. edt. http://id.loc.gov/vocabulary/relators/edt Faralli, Stefano. editor. https://orcid.org/0000-0003-3684-8815. edt. http://id.loc.gov/vocabulary/relators/edt Marras, Mirko. editor. https://orcid.org/0000-0003-1989-6057. edt. http://id.loc.gov/vocabulary/relators/edt Stilo, Giovanni. editor. https://orcid.org/0000-0002-2092-0213. edt. http://id.loc.gov/vocabulary/relators/edt SpringerLink (Online service) Springer Nature eBook Springer Nature eBook Printed edition 9783030788179 Printed edition 9783030788193 Communications in Computer and Information Science 1865-0929 1418.