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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 8 of 8 1 Dataset diversity: measuring and mitigating geographical bias in image search and retrieval Mandal, Abhishek; Leavy, Susan; Little, Suzanne In: Mandal, Abhishek, Leavy, Susan and Little, Suzanne orcid:0000-0003-3281-3471 (2021) Dataset diversity: measuring and mitigating geographical bias in image search and retrieval. In: 1st International Workshop on Trustworthy AI for Multimedia Computing, 24 Oct 2021, Chengdu, China. ISBN 978-1-4503-8674-6 (2021) BASE Show details 2 Mitigating Gender Bias in Machine Learning Data Sets Leavy, Susan; Meaney, Gerardine; Wade, Karen. - : Springer, 2021 BASE Show details 3 Data, Power and Bias in Artificial Intelligence Leavy, Susan; O'Sullivan, Barry; Siapera, Eugenia. - 2021 BASE Show details 4 Uncovering Gender Bias in Media Coverage of Politicians with Machine Learning ... Leavy, Susan. - : arXiv, 2020 BASE Show details 5 Mitigating Gender Bias in Machine Learning Data Sets ... Leavy, Susan; Meaney, Gerardine; Wade, Karen. - : arXiv, 2020 BASE Show details 6 Navigating Literary Text with Word Embeddings and Semantic Lexicons Leavy, Susan; Wade, Karen; Meaney, Gerardine; Greene, Derek. - 2019 Abstract: Workshop on Computational Methods in the Humanities 2018 (COMHUM 2018), Luasanne, Switzerland, 4-5 June 2018 ; Word embeddings represent a powerful tool for mining the vocabularies of literary and historical text. However, there is little research demonstrating appropriate strategies for representing text and setting parameters, when constructing embedding models within a digital humanities context. In this paper we examine the effects of these choices using a case study involving 18th and 19th century texts from the British Library. The study demonstrates the importance of examining implicit assumptions around default strategies, when using embeddings with literary texts and highlights the potential of quantitative analysis to inform critical analysis ; Irish Research Council Keyword: British Library; Critical analysis; Digital humanities; Eighteenth century; Nineteenth century; Representing text; Setting parameters; Vocabularies; Word embeddings URL: http://hdl.handle.net/10197/10461 BASE Hide details 7 Systems in Language: Text Analysis of Government Reports of the Irish Industrial School System with Word Embedding Leavy, Susan; Pine, Emilie; Keane, Mark T.. - : Oxford University Press, 2019 BASE Show details 8 Uncovering gender bias in newspaper coverage of Irish politicians using machine learning Leavy, Susan. - : Oxford University Press, 2019 BASE Show details

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