Adhering, steering, and queering: treatment of gender in Natural Language Generation

Yolande Strengers, Lizhen Qu, Qiongkai Xu, Jarrod Knibbe

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

23 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationProceedings of the 2020 CHI Conference on Human Factors in Computing Systems
EditorsJoanna McGrenere, Andy Cockburn
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages14
ISBN (Electronic)9781450367080
Publication statusPublished - 21 Apr 2020
EventInternational Conference on Human Factors in Computing Systems 2020 - Honolulu , United States of America
Duration: 25 Apr 202030 Apr 2020
Conference number: 38th (Website) (Proceedings)

Publication series

NameConference on Human Factors in Computing Systems - Proceedings


ConferenceInternational Conference on Human Factors in Computing Systems 2020
Abbreviated titleCHI 2020
Country/TerritoryUnited States of America
Internet address


  • feminist hci
  • natural language generation

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