Gender Recognition or Gender Reductionism?

Hamidi, Foad; Scheuerman, Morgan Klaus; Branham, Stacy M.

doi:10.1145/3173574.3173582

Cited by 133 publications

(43 citation statements)

References 28 publications

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“…Among other goals, trans competent interaction design recognizes that design decisions can disproportionately harm trans people; makes changes to minimize this harm; and understands that users have unique identities that may change over time [2]. Recent HCI work has moved towards trans competence by understanding trans people's perceptions toward automatic gender recognition systems, which is disproportionately harmful for trans users [43], considering how to account for non-binary gender variation in online communities [50], and designing an application to support safe restroom access for trans people [7]. Building on previous research about gender transition on social media [39][40][41], with the current research I address the trans competent interaction design [2] agenda by providing in-depth analysis about the contexts in which trans people use social media during gender transition.…”

Section: Trans Identities and Experiences Onlinementioning

confidence: 99%

Social Media as Social Transition Machinery

Haimson

2018

Proc. ACM Hum.-Comput. Interact.

104

100

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Social media, and people's online self-presentations and social networks, add complexity to people's experiences managing changing identities during life transitions. I use gender transition as a case study to understand how people experience liminality on social media. I qualitatively analyzed data from transition blogs on Tumblr (n=240), a social media blogging site on which people document their gender transitions, and in-depth interviews with transgender bloggers (n=20). I apply ethnographer van Gennep's liminality framework to a social media context and contribute a new understanding of liminality by arguing that reconstructing one's online identity during life transitions is a rite of passage. During life transitions, people present multiple identities simultaneously on different social media sites that together comprise what I call social transition machinery. Social transition machinery describes the ways that, for people facing life transitions, multiple social media sites and networks often remain separate, yet work together to facilitate life transitions. CCS Concepts: • Human-centered computing~HCI theory, concepts and models • Human-centered computing~Collaborative and social computing theory, concepts and paradigms • Human-centered computing~Social media • Human-centered computing~Empirical studies in collaborative and social computing

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Section: Trans Identities and Experiences Onlinementioning

confidence: 99%

Social Media as Social Transition Machinery

Haimson

2018

Proc. ACM Hum.-Comput. Interact.

104

100

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“…Participants suggested the need for solutions, consisting of labels at doors and next to cameras that serve as a privacy policy ("They should outline what data is captured, how it is processed and why", P11), and opportunities for the public to review and amend the data captured by analysis technologies, such as by way of mobile applications (P11). Similar recommendations were made in the context of research on gender recognition, encouraging the support of self-expression and autonomy by way of defining and modifying one's own gender identity [43].…”

Section: Transparency Consent and Well-beingmentioning

confidence: 81%

“…We had anticipated response from users with regards to gender representation, but failed to collect any. This observation does not mean there is currently no debate or concern about binary gender representation [43,75,53]. Instead, we believe that while our approach was successful in attracting spontaneous users and stimulating public discussion, it also introduced the challenge to reach a heterogeneous sample of the population.…”

Section: Limitationsmentioning

confidence: 93%

Biometric Mirror

Wouters

Kelly

Velloso

et al. 2019

Proceedings of the 2019 on Designing Interactive Systems Conference

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Facial analysis applications are increasingly being applied to inform decision-making processes. However, as global reports of unfairness emerge, governments, academia and industry have recognized the ethical limitations and societal implications of this technology. Alongside initiatives that aim to formulate ethical frameworks, we believe that the public should be invited to participate in the debate. In this paper, we discuss Biometric Mirror, a case study that explored opinions about the ethics of an emerging technology. The interactive application distinguished demographic and psychometric information from people's facial photos and presented speculative scenarios with potential consequences based on their results. We analyzed the interactions with Biometric Mirror and media reports covering the study. Our findings demonstrate the nature of public opinion about the technology's possibilities, reliability, and privacy implications. Our study indicates an opportunity for case study-based digital ethics research, and we provide practical guidelines for designing future studies.

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“…Being misgendered is a form of mockery, imposing a literal challenge to the cis-gender person's stated gender and increasing one's perception of being socially marginalized. Misgendering transgender people could result in decreased selfworth and self-esteem, increased level of dysphoria, which could take many in this community to experience depression and even suicidal thoughts [20].…”

Section: Limitations Of Binary Gender Classi Cationmentioning

confidence: 99%

Gender Classification and Bias Mitigation in Facial Images

Wang

Protopapas

Yang

et al. 2020

12th ACM Conference on Web Science

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Gender classi cation algorithms have important applications in many domains today such as demographic research, law enforcement, as well as human-computer interaction. Recent research showed that algorithms trained on biased benchmark databases could result in algorithmic bias. However, to date, li le research has been carried out on gender classi cation algorithms' bias towards gender minorities subgroups, such as the LGBTQ and the non-binary population, who have distinct characteristics in gender expression. In this paper, we began by conducting surveys on existing benchmark databases for facial recognition and gender classi cation tasks. We discovered that the current benchmark databases lack representation of gender minority subgroups. We worked on extending the current binary gender classi er to include a non-binary gender class. We did that by assembling two new facial image databases: 1) a racially balanced inclusive database with a subset of LGBTQ population 2) an inclusive-gender database that consists of people with non-binary gender. We worked to increase classi cation accuracy and mitigate algorithmic biases on our baseline model trained on the augmented benchmark database. Our ensemble model has achieved an overall accuracy score of 90.39%, which is a 38.72% increase from the baseline binary gender classi er trained on Adience. While this is an initial a empt towards mitigating bias in gender classi cation, more work is needed in modeling gender as a continuum by assembling more inclusive databases.

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Gender Recognition or Gender Reductionism?

Cited by 133 publications

References 28 publications

Social Media as Social Transition Machinery

Social Media as Social Transition Machinery

Biometric Mirror

Gender Classification and Bias Mitigation in Facial Images

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