Author ORCID Identifier

https://orcid.org/0000-0002-2768-2676

Document Type

Conference Paper

Rights

Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence

Disciplines

Computer Sciences, Sociology, Women's and gender studies

Publication Details

Irish Conference on Artificial Intelligence and Cognitive Science (AICS 2022)

https://link.springer.com/book/9783031264399

Abstract

Predictions from machine learning models can reflect biases in the data on which they are trained. Gender bias has been identified in natural language processing systems such as those used for recruitment. The development of approaches to mitigate gender bias in training data typically need to be able to isolate the effect of gender on the output to see the impact of gender. While it is possible to isolate and identify gender for some types of training data, e.g. CVs in recruitment, for most textual corpora there is no obvious gender label. This paper proposes a general approach to measure bias in textual training data for NLP prediction systems by providing a gender label identified from the textual content of the training data. The approach is compared with the identity term template approach currently in use, also known as Gender Bias Evaluation Datasets (GBETs), which involves the design of synthetic test datasets which isolate gender and are used to probe for gender bias in a dataset. We show that our Identity Term Sampling (ITS) approach is capable of identifying gender bias at least as well as identity term templates and can be used on training data that has no obvious gender label.

DOI

https://doi.org/10.21427/BKM6-RF06

Funder

Science Foundation Ireland

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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