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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. - 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 Abstract: Industrial Memories is a digital humanities initiative to supplement close readings of a government report with new distant readings, using text analytics techniques. The Ryan Report (2009), the official report of the Commission to Inquire into Child Abuse (CICA), details the systematic abuse of thousands of children 15 from 1936 to 1999 in residential institutions run by religious orders and funded and overseen by the Irish State. Arguably, the sheer size of the Ryan Report—over 1 million words— warrants a new approach that blends close readings to witness its findings, with distant readings that help surface system-wide findings embedded in the Report. Although CICA has been lauded internationally for 20 its work, many have critiqued the narrative form of the Ryan Report, for obfuscating key findings and providing poor systemic, statistical summaries that are crucial to evaluating the political and cultural context in which the abuse took place (Keenan, 2013, Child Sexual Abuse and the Catholic Church: Gender, Power, and Organizational Culture. Oxford University Press). In this article, we concentrate on describing the distant reading methodology we adopted, using machine learning and text-analytic methods and report on what they surfaced from the 2 Report. The contribution of this work is threefold: (i) it shows how text analytics can be used to surface new patterns, summaries and results that were not apparent via close reading, (ii) it demonstrates how machine learning can be used to annotate text by using word embedding to compile domain-specific semantic lexicons for feature extraction and (iii) it demonstrates how digital humanities methods can be applied to an official state inquiry with social justice impact. ; Science Foundation Ireland ; Insight Research Centre Keyword: Child Sexual Abuse; Digital humanities; Machine learning; Ryan Report; Social justice; Text analytics URL: http://hdl.handle.net/10197/10884 https://doi.org/10.1093/llc/fqz012 BASE Hide 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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