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Crime and Relationship : Exploring Gender Bias in NLP Corpora

Year: 2020

Type: conference-paper

Abstract: Gender bias in natural language processing (NLP) tools, deriving from implicit human bias embedded in language data, is an important and complicated problem on the road to fair algorithms. We leverage topic modeling to retrieve documents associated with particular gendered categories, and discuss how exploring these documents can inform our understanding of the corpora we may use to train NLP tools. This is a starting point for challenging the systemic power structures and producing a justice-focused approach to NLP.

Source: KTH Publication Database DiVA (KTH Royal Institute of Technology)

Authors: Devinney, Hannah, Björklund, Jenny, Björklund, Henrik

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDGs: Reduced inequalities, Gender equality, Peace, justice, and strong institutions

Open Access status: green

OpenAlex ID: W7055673692

FWCI:

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Related to: 10