Designed & Discovered Euphoria: Insights from Trans-Femme Players' Experiences of Gender Euphoria in Video Games
説明

Many transgender (and cisgender) people experience gender euphoria -- satisfaction and relief caused by self-actualization and gender congruence -- a term that has been overlooked by the design community. Video games create intense experiences involving identities, bodies, and social interaction, providing opportunities to empower people through gender euphoria. We develop themes for creating and supporting gender euphoria in games within the Design, Dynamics, Experience Game Design Framework from a reflexive thematic analysis of 25 games, with an in-depth analysis of four of them. The analysis combines the authors' positionalities as trans gamers with close reading and content analysis of the games, employing perspectives from critical discourse analysis. We contribute an operational understanding of gender euphoria to support design, in-depth case studies of particularly euphoric game experiences, and identify themes that designers and researchers can use to develop new games and analyze existing ones.

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Saying No to "Yes Means Yes": Limitations of Affirmative Consent for Mitigating Unwanted Behavior Online According to Women and LGBTQIA+ Stakeholders
説明

Affirmative consent—or “yes means yes”—was initially devised to mitigate sexual violence stemming from misunderstandings of consent. More recently, HCI research has considered adapting affirmative consent to mitigate nonconsensual acts online. Given that affirmative consent has historically been under-adopted and critiqued as unrealistic in its original context of in-person sexual activity, it is imperative that users be involved in producing guidance for affirmative consent practice in computer-mediated contexts. We report a focus group study about affirmative consent in VR dating with 16 stakeholders identifying as women and/or LGBTQIA+ (demographics at elevated risk of nonconsensual acts). Findings suggest that affirmative consent may be obsolete: participants elucidated several reasons why affirmative consent is impractical, if not impossible, to practice in virtual environments. Participants offered provocations to guide creation of new, inherently computer-mediated consent models for mitigating unwanted acts, posing significant opportunity for HCI to have public health impact.

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Hidden in Plain Sight: a Structured Analysis of Privacy Policies in the Context of Body-worn 'FemTech' Technologies
説明

As HCI research turns to women's reproductive health as a topic of interest, an increasing number of female-oriented technologies (FemTech) are being marketed to consumers. This opens up a space for better management and understanding of intimate health but is not without risk. Reproductive health data collected by FemTech devices is highly sensitive and politicized. Breaches of privacy can cause or exacerbate discrimination and gender inequality, and negatively impact users' safety and well-being. It is therefore important that users are well informed about how their data is collected, handled, used and stored. This work contributes insights into whether and to what extent this is achieved by current FemTech. We conduct a structured content analysis of 18 in-effect privacy policies. Applying an empirically-grounded taxonomy, we identify challenges in policy wording, content and presentation. We conclude with recommendations for improving transparency and supporting users in providing informed consent and claiming data authority.

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“I am a Technology Creator”: Black Girls as Technosocial Change Agents in a Culturally-Responsive Robotics Camp
説明

Black girls and women have long been creators in computing spaces. However, much computing education positions Black girls as workers who execute tasks for others' purposes. Our work takes a different approach by positioning Black girls as technosocial change agents who challenge dominant narratives and construct more liberating identities and social relations as they create new technologies. We draw on data from seven Black girls, ages 9-12, who participated in a 20-hour culturally responsive computing (CRC) camp focused on robotics. Using a thematic analysis approach, we explore how these Black girls demonstrate and enhance their technosocial change agency (TSCA) throughout the camp. We identify themes related to how creating technology helps Black girls refine and fulfill their definitions of technical creators and develop agency through technology creation. We discuss computing education and technology design recommendations within the TSCA framework to support learners' emerging TSCA in future CRC programs.

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The Unintended Costs of Platform Interventions: Black-Owned Restaurants and Yelp Reputation
説明

In Spring 2020, digital review-based platform Yelp added the searchable ``Black-owned'' attribute to support Black-owned businesses. Based on the literature, the impacts of this design intervention were mixed. As such, we sourced an original dataset of 250,000+ Yelp reviews from Black and non-Black-owned restaurants in Detroit and Los Angeles. Performing statistical and trend analyses, we compared the reputation metrics of Black-owned restaurants to their non-Black-owned counterparts before and after the intervention. Although Yelp reported positive impacts, our results contribute to the growing evidence of the harms and unintended costs of platform interventions. Specifically, while awareness of Black ownership and the number of Black-owned restaurant reviews increased, assumedly among and by Yelp’s predominately non-Black users, Black-owned restaurants saw a decline in average star ratings. Altogether, the findings highlight the need to interrogate underlying assumptions in the design process, integrating critical race concepts to better contextualize and evaluate interventions targeting marginalized users.

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Why Can’t Black Women Just Be?: Black Femme Content Creators Navigating Algorithmic Monoliths
説明

Content creation allows many online social media users to support

themselves financially through creativity. The “creator economy”

empowers individuals to create content (i.e. lifestyle, fitness, beauty)

about their interests, hobbies and daily life. Social media platforms

in turn moderate content (e.g., banning accounts, flagging and re-

porting videos) to create safer online communities. However, Black

women, femme, and non-binary people content creators have seen

their content disproportionately suppressed, thus limiting their

success on the platform. In this paper, we investigate Black femme

content creators’ (BFCC) theories about how their identities impact

both how they create content and how that content is subsequently

moderated. In our findings, we share the perceptions participants

felt the algorithm constrains Black creators to. We build upon Crit-

ical Technocultural Discourse studies and algorithmic folk theories

attributed to Black women and non-binary content creators to ex-

plore how Black joy can be prioritized online to resist algorithmic

monoliths.

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“It feels like we're not meeting the criteria": Examining and Mitigating the Cascading Effects of Bias in Automatic Speech Recognition in Spoken Language Interfaces.
説明

Researchers have demonstrated that Automatic Speech Recognition (ASR) systems perform differently across demographic groups (i.e. show bias), yet their downstream impact on spoken language interfaces remains unexplored. We examined this question in the context of a real-world AI-powered interface that provides tutors with feedback on the quality of their discourse. We found that the Whisper ASR had lower accuracy for Black vs. white tutors, likely due to differences in acoustic patterns of speech. The downstream automated discourse classifiers of tutor talk were correspondingly less accurate for Black tutors when presented with ASR input. As a result, although Black tutors demonstrated higher-quality discourse on human transcripts, this trend was not evident on ASR transcripts. We experimented with methods to reduce ASR bias, finding that fine-tuning the ASR on Black speech reduced, but did not eliminate, ASR bias and its downstream effects. We discuss implications for AI-based spoken language interfaces aimed at providing unbiased assessments to improve performance outcomes.

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