From Language to Algorithm: Transphobia in Research on Gender Recognition Software

Presenter Title/Affiliation

University of British Columbia

Start Date

23-5-2021 1:30 PM

Event Name

Panel discussion

Panel Number

24

Panel Chair Name

David Peterson

Zoom URL to Join

https://ciis.zoom.us/j/99469567105

Zoom Meeting ID

994 6956 7105

Abstract

Within the North American blogosphere, LGBTQ+ scholars and activists are mounting vocal criticism of the technological drive towards ever more detailed applications of facial recognition and gender identification software (Gault, 2019; Gutierrez, 2019; Hay, 2019; Johnson, 2019; Merlan & Mehrotra, 2019; Samuel, 2019). Much of that critical attention is driven by a small number of scholars in studies of technology and human-computer interaction who challenge the underlying conceptions of gender which inform such software (Hamidi, Scheuerman, & Branham, 2018; Kannabiran & Petersen, 2010; Rode, 2011). Such work has explored the history of algorithmic bias against trans identities (Hicks, 2019), the experiences and strategies trans people use when navigating prescriptive gender norms of technological systems (Ahmed, 2018), and the risks for trans lives that are created by binary and immutable conceptions of gender recognition algorithms (Keyes, 2018). The fact that commercial development of facial recognition software interacts with research practices presents the language employed in research publications as an avenue for examining the presence of trans-inclusive language and trans-competent research design in the development of gender recognition software. Our project investigates the language of facial recognition research publications which mention non-binary gender, gender non-conformity, and gender transition in some way. We are interested in how choices of phrases and citations do or do not draw on trans experience and trans voices, or do or do not link lto scholarship on gender and trans identity (Thieme & Saunders, 2018). For our analysis, we have collected via database searches a corpus of 15 conference and research articles published between 2010 to 2019; we conduct a content analysis with a basic scheme for coding. Given the overwhelming assumptions of binary and immutable conceptions of gender which have been shown to lie at the heart of most technological work on gender recognition (Keyes, 2018), we ask: how do trans, non-binary, or genderfluid identities figure in the research discourse that does mention gender outside an exclusively binary or immutable conception of gender? And how are practices of citation used to characterize the landscape of existing research that is presented as relevant to projects of gender recognition?

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May 23rd, 1:30 PM May 23rd, 2:00 PM

From Language to Algorithm: Transphobia in Research on Gender Recognition Software

Within the North American blogosphere, LGBTQ+ scholars and activists are mounting vocal criticism of the technological drive towards ever more detailed applications of facial recognition and gender identification software (Gault, 2019; Gutierrez, 2019; Hay, 2019; Johnson, 2019; Merlan & Mehrotra, 2019; Samuel, 2019). Much of that critical attention is driven by a small number of scholars in studies of technology and human-computer interaction who challenge the underlying conceptions of gender which inform such software (Hamidi, Scheuerman, & Branham, 2018; Kannabiran & Petersen, 2010; Rode, 2011). Such work has explored the history of algorithmic bias against trans identities (Hicks, 2019), the experiences and strategies trans people use when navigating prescriptive gender norms of technological systems (Ahmed, 2018), and the risks for trans lives that are created by binary and immutable conceptions of gender recognition algorithms (Keyes, 2018). The fact that commercial development of facial recognition software interacts with research practices presents the language employed in research publications as an avenue for examining the presence of trans-inclusive language and trans-competent research design in the development of gender recognition software. Our project investigates the language of facial recognition research publications which mention non-binary gender, gender non-conformity, and gender transition in some way. We are interested in how choices of phrases and citations do or do not draw on trans experience and trans voices, or do or do not link lto scholarship on gender and trans identity (Thieme & Saunders, 2018). For our analysis, we have collected via database searches a corpus of 15 conference and research articles published between 2010 to 2019; we conduct a content analysis with a basic scheme for coding. Given the overwhelming assumptions of binary and immutable conceptions of gender which have been shown to lie at the heart of most technological work on gender recognition (Keyes, 2018), we ask: how do trans, non-binary, or genderfluid identities figure in the research discourse that does mention gender outside an exclusively binary or immutable conception of gender? And how are practices of citation used to characterize the landscape of existing research that is presented as relevant to projects of gender recognition?

https://digitalcommons.ciis.edu/lavlang/2021/sunday/20