Titlebook: Advances in Bias and Fairness in Information Retrieval; Third International Ludovico Boratto,Stefano Faralli,Giovanni Stilo Conference pro

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Palgrave Studies in British Musical Theatre systems typically rely on machine learning algorithms that solve the classification task. Although the problem of fairness has been widely addressed in binary classification, unfortunately, the fairness of multi-class classification problem needs to be further investigated lacking well-established

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The Lane and ENSA Headquarters (1939),ful forms of bias, such as with respect to gender or race. In this paper, we consider the problem of ., in which the goal is to suggest keywords that are relevant to a user’s search query, but exhibit less (or opposite) bias. We present a multi-objective optimization method that uses word embeddings

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Communications in Computer and Information Sciencehttp://image.papertrans.cn/a/image/146850.jpg

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Advances in Bias and Fairness in Information Retrieval978-3-031-09316-6Series ISSN 1865-0929 Series E-ISSN 1865-0937

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Conference proceedings 2022ril, 2022. .The 9 full papers and 4 short papers were carefully reviewed and selected from 34 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. .

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Front Matterzum Beispiel Religiosität, Umweltbewusstsein oder Rechtsextremismus, häufig nicht mit einer einzigen Frage im Fragebogen erhoben werden. Stattdessen überlegen sich Forscher eine Reihe von manifesten Aussagen (sog. Items), die das interessierende Konstrukt abbilden sollen (z.B. die Frage „das Leben h

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