LEADER 04094nam 22006255 450 001 996546825403316 005 20230714050258.0 010 $a3-031-37249-2 024 7 $a10.1007/978-3-031-37249-0 035 $a(MiAaPQ)EBC30645962 035 $a(Au-PeEL)EBL30645962 035 $a(DE-He213)978-3-031-37249-0 035 $a(PPN)272251291 035 $a(EXLCZ)9927565161300041 100 $a20230714d2023 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$b[electronic resource] $e4th International Workshop, BIAS 2023, Dublin, Ireland, April 2, 2023, Revised Selected Papers /$fedited by Ludovico Boratto, Stefano Faralli, Mirko Marras, Giovanni Stilo 205 $a1st ed. 2023. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2023. 215 $a1 online resource (187 pages) 225 1 $aCommunications in Computer and Information Science,$x1865-0937 ;$v1840 311 08$aPrint version: Boratto, Ludovico Advances in Bias and Fairness in Information Retrieval Cham : Springer International Publishing AG,c2023 9783031372483 327 $aA Study on Accuracy, Miscalibration, and Popularity Bias in Recommendations -- Measuring Bias in Multimodal Models: Multimodal Composite Association Score -- Evaluating Fairness Metrics -- Utilizing Implicit Feedback for User Mainstreaminess Evaluation and Bias Detection in Recommender Systems -- Preserving Utility in Fair Top-k Ranking with Intersectional Bias -- Mitigating Position Bias in Hotels Recommender Systems -- Improving Recommender System Diversity with Variational Autoencoders -- Addressing Biases in the Texts using an End-to-End Pipeline Approach -- Bootless Application of Greedy Re-ranking Algorithms in Fair Neural Team Formation -- How do you feel? Information Retrieval in Psychotherapy and Fair Ranking Assessment -- Understanding Search Behavior Bias in Wikipedia -- Do you MIND? Reflections on the MIND dataset for research on diversity in news recommendations -- Detecting and Measuring Social Bias of Arabic Generative Models in the Context of Search and Recommendation -- What are we missing in algorithmic fairness? Discussing open challenges for fairness analysis in user profiling with Graph Neural Networks. 330 $aThis book constitutes the refereed proceedings of the 4th International Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2023, held in Dublin, Ireland, in April 2023. The 10 full papers and 4 short papers included in this book were carefully reviewed and selected from 36 submissions. The present recent research in the following topics: biases exploration and assessment; mitigation strategies against biases; biases in newly emerging domains of application, including healthcare, Wikipedia, and news, novel perspectives; and conceptualizations of biases in the context of generative models and graph neural networks. 410 0$aCommunications in Computer and Information Science,$x1865-0937 ;$v1840 606 $aComputer engineering 606 $aComputer networks 606 $aArtificial intelligence 606 $aElectronic commerce 606 $aComputer Engineering and Networks 606 $aArtificial Intelligence 606 $ae-Commerce and e-Business 615 0$aComputer engineering. 615 0$aComputer networks. 615 0$aArtificial intelligence. 615 0$aElectronic commerce. 615 14$aComputer Engineering and Networks. 615 24$aArtificial Intelligence. 615 24$ae-Commerce and e-Business. 676 $a025.524 700 $aBoratto$b Ludovico$01373698 701 $aFaralli$b Stefano$01373699 701 $aMarras$b Mirko$01373700 701 $aStilo$b Giovanni$01373701 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996546825403316 996 $aAdvances in Bias and Fairness in Information Retrieval$93404763 997 $aUNISA