Titlebook: Bias and Social Aspects in Search and Recommendation; First International Ludovico Boratto,Stefano Faralli,Giovanni Stilo Conference proce

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Zhiwei Tang,Lingling Hu,Jianye Lia for machine learning. The work draws upon gender theory and sociolinguistics to systematically indicate levels of bias in textual training data and associated neural word embedding models, thus highlighting pathways for both removing bias from training data and critically assessing its impact in the context of search and recommender systems.

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