research-article

Adhering, Steering, and Queering: Treatment of Gender in Natural Language Generation

Authors:

Yolande Strengers,

Jarrod KnibbeAuthors Info & Claims

CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Pages 1 - 14

https://doi.org/10.1145/3313831.3376315

Published: 23 April 2020 Publication History

Abstract

Natural Language Generation (NLG) supports the creation of personalized, contextualized, and targeted content. However, the algorithms underpinning NLG have come under scrutiny for reinforcing gender, racial, and other problematic biases. Recent research in NLG seeks to remove these biases through principles of fairness and privacy. Drawing on gender and queer theories from sociology and Science and Technology studies, we consider how NLG can contribute towards the advancement of gender equity in society. We propose a conceptual framework and technical parameters for aligning NLG with feminist HCI qualities. We present three approaches: (1) adhering to current approaches of removing sensitive gender attributes, (2) steering gender differences away from the norm, and (3) queering gender by troubling stereotypes. We discuss the advantages and limitations of these approaches across three hypothetical scenarios; newspaper headlines, job advertisements, and chatbots. We conclude by discussing considerations for implementing this framework and related ethical and equity agendas.

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Cited By

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https://doi.org/10.1155/2024/3238737
Tran ARothschild AKender KOsipova EKinnee BTaylor JMeyer LHaimson OLight ADisalvo C(2024)Making Trouble: Techniques for Queering Data and AI SystemsCompanion Publication of the 2024 ACM Designing Interactive Systems Conference10.1145/3656156.3658393(381-384)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3656156.3658393
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Index Terms

Adhering, Steering, and Queering: Treatment of Gender in Natural Language Generation
1. Human-centered computing
  1. Human computer interaction (HCI)

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cover image ACM Conferences

CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

April 2020

10688 pages

ISBN:9781450367080

DOI:10.1145/3313831

General Chairs:
Regina Bernhaupt
Eindhoven University of Technology, Netherlands
,
Florian 'Floyd' Mueller
Monash University, Australia
,
David Verweij
Newcastle University, UK
,
Josh Andres
RMIT, Australia
,
Program Chairs:
Joanna McGrenere
University of British Columbia, Canada
,
Andy Cockburn
University of Canterbury, New Zealand
,
Ignacio Avellino
University of Maryland Baltimore County, USA
,
Alix Goguey
Grenoble Alpes University, France
,
Pernille Bjørn
University of Copenhagen, Denmark
,
Shengdong (Shen) Zhao
National University of Singapore, Singapore
,
Briane Paul Samson
Future University Hakodate, Japan & De La Salle University, Philippines
,
Rafal Kocielnik
University of Washington, USA

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Published: 23 April 2020

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https://doi.org/10.1155/2024/3238737
Tran ARothschild AKender KOsipova EKinnee BTaylor JMeyer LHaimson OLight ADisalvo C(2024)Making Trouble: Techniques for Queering Data and AI SystemsCompanion Publication of the 2024 ACM Designing Interactive Systems Conference10.1145/3656156.3658393(381-384)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3656156.3658393
Zhang LLiao XYang ZGao BWang CYang QLi D(2024)Partiality and Misconception: Investigating Cultural Representativeness in Text-to-Image ModelsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642877(1-25)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642877
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