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Cited by 284

The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.

Gender bias in artificial intelligence: the need for diversity and gender theory in machine learning

Leavy, Susan·2018·Proceedings of the 1st International Workshop on Gender Equality in Software Engineering
DOI 10.1145/3195570.3195580
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Abstract

Artificial intelligence is increasingly influencing the opinions and behavior of people in everyday life. However, the over-representation of men in the design of these technologies could quietly undo decades of advances in gender equality. Over centuries, humans developed critical theory to inform decisions and avoid basing them solely on personal experience. However, machine intelligence learns primarily from observing data that it is presented with. While a machine's ability to process large volumes of data may address this in part, if that data is laden with stereotypical concepts of gender, the resulting application of the technology will perpetuate this bias. While some recent studies sought to remove bias from learned algorithms they largely ignore decades of research on how gender ideology is embedded in language. Awareness of this research and incorporating it into approaches to machine learning from text would help prevent the generation of biased algorithms. Leading thinkers in the emerging field addressing bias in artificial intelligence are also primarily female, suggesting that those who are potentially affected by bias are more likely to see, understand and attempt to resolve it. Gender balance in machine learning is therefore crucial to prevent algorithms from perpetuating gender ideologies that disadvantage women.

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Metadata

Authors
Leavy, S.
Venue
Proceedings of the 1st International Workshop on Gender Equality in Software Engineering
Publisher
ACM
Published
2018-05-28
Pages
14-16
DOI
10.1145/3195570.3195580
OpenAlex
W2810135932
Cited by
284
References
23
FWCI
32.59
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Gender bias in artificial intelligence: the need for diversity and gender theory in machine learning. https://app.cochat.ai/papers/10.1145/3195570.3195580
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Leavy, S. (2018). Gender bias in artificial intelligence: the need for diversity and gender theory in machine learning. Proceedings of the 1st International Workshop on Gender Equality in Software Engineering. https://doi.org/10.1145/3195570.3195580

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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