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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; Greene, Derek. - : arXiv, 2020 Abstract: Artificial Intelligence has the capacity to amplify and perpetuate societal biases and presents profound ethical implications for society. Gender bias has been identified in the context of employment advertising and recruitment tools, due to their reliance on underlying language processing and recommendation algorithms. Attempts to address such issues have involved testing learned associations, integrating concepts of fairness to machine learning and performing more rigorous analysis of training data. Mitigating bias when algorithms are trained on textual data is particularly challenging given the complex way gender ideology is embedded in language. This paper proposes a framework for the identification of gender bias in training data 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 ... : 10 pages, 5 figures, 5 Tables, Presented as Bias2020 workshop (as part of the ECIR Conference) - http://bias.disim.univaq.it ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG URL: https://arxiv.org/abs/2005.06898 https://dx.doi.org/10.48550/arxiv.2005.06898 BASE Hide details 6 Navigating Literary Text with Word Embeddings and Semantic Lexicons Leavy, Susan; Wade, Karen; Meaney, Gerardine. - 2019 BASE Show 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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