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Other research product . 2020

Mitigating Gender Bias in Machine Learning Data Sets

Leavy, Susan; Meaney, Gerardine; Wade, Karen; Greene, Derek;
Open Access
Published: 12 Jul 2020
Publisher: Springer
Country: Ireland

International Workshop on Algorithmic Bias in Search and Recommendation (Bias 2020), Lisbon, Portugal (held online due to coronavirus outbreak) 14 April 2020 Algorithmic bias has the capacity to amplify and perpetuate societal bias, and presents profound ethical implications for society. Gender bias in algorithms 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 assessing its impact in the context of search and recommender systems. Irish Research Council Science Foundation Ireland

Subjects by Vocabulary

ACM Computing Classification System: ComputingMilieux_THECOMPUTINGPROFESSION


Algorithmic bias, Gender, Machine learning, Natural language processing

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