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BRIEF RESEARCH REPORT article

Front. Hum. Dyn.
Sec. Digital Impacts
Volume 6 - 2024 | doi: 10.3389/fhumd.2024.1495270
This article is part of the Research Topic #breakthebias: Working Towards Alternative Ways of Being in a Digital World Through Conversations With Critical Friends, Texts, and Technologies View all 4 articles

A Perspective on Gender Bias in Generated Text Data

Provisionally accepted
Thomas Hupperich Thomas Hupperich *
  • University of Münster, Münster, Germany

The final, formatted version of the article will be published soon.

    Text generation by artificial intelligence became available to a broader public, latterly. This technology is based on machine learning and language models that need to be trained with input data. Many studies have focused on the distinction of human-written text. versus generated texts but recent studies show that the underlying language models might be prone to reproduce gender bias in their output and, consequently, reinforcing gender roles and imbalances.In this paper, we give a perspective on this topic, considering both the generated text data itself and the machine learning models used for language generation. We present a case study of gender bias in generated text data and review recent literature addressing language models. Our results indicate that researching gender bias in the context of text generation faces significant challenges and that future work needs to overcome a lack of definitions as well as a lack of transparency.

    Keywords: gender bias, Generative artificial intelligence, Text generation, Language models, Machine Lear ning

    Received: 12 Sep 2024; Accepted: 10 Dec 2024.

    Copyright: © 2024 Hupperich. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Thomas Hupperich, University of Münster, Münster, Germany

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.