LEADER 03382nam 22005055 450 001 9910488695103321 005 20251225175029.0 010 $a3-030-78818-0 024 7 $a10.1007/978-3-030-78818-6 035 $a(CKB)5590000000516476 035 $a(MiAaPQ)EBC6676225 035 $a(Au-PeEL)EBL6676225 035 $a(PPN)25806062X 035 $a(BIP)80723392 035 $a(BIP)80231323 035 $a(DE-He213)978-3-030-78818-6 035 $a(EXLCZ)995590000000516476 100 $a20210624d2021 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvances in Bias and Fairness in Information Retrieval $eSecond International Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2021, Lucca, Italy, April 1, 2021, Proceedings /$fedited by Ludovico Boratto, Stefano Faralli, Mirko Marras, Giovanni Stilo 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021. 215 $a1 online resource (181 pages) 225 1 $aCommunications in Computer and Information Science,$x1865-0937 ;$v1418 311 08$a3-030-78817-2 327 $aTowards 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. 330 $aThis book constitutes refereed proceedings of the Second International Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2021, held in April, 2021. Due to the COVID-19 pandemic BIAS 2021 was held virtually. The 11 full papers and 3 short papers were carefully reviewed and selected from 37 submissions. The papers cover topics that go from search and recommendation in online dating, education, and social media, over the impact of gender bias in word embeddings, to tools that allow to explore bias and fairnesson the Web. . 410 0$aCommunications in Computer and Information Science,$x1865-0937 ;$v1418 606 $aDatabase management 606 $aDatabase Management System 615 0$aDatabase management. 615 14$aDatabase Management System. 676 $a025.04 702 $aBoratto$b Ludovico 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910488695103321 996 $aAdvances in Bias and Fairness in Information Retrieval$92883274 997 $aUNINA