Sheena Erete

As generative AI (GenAI) technologies become increasingly integrated into professional settings, it is critical to understand how these tools are experienced by individuals who are more likely to encounter harm, violence, and trauma in the workplace. Black women, in particular, continue to face systemic discrimination in hiring, promotion, and everyday interactions within predominantly white work environments. In this paper, we center the voices of 21 Black women working in the U.S. tech sector to explore how they engage with GenAI technologies to navigate workplace dynamics. Drawing on Black feminist epistemologies, our findings suggest that these women use GenAI as survival tools that help them manage hostile work environments and negotiate complex social interactions. GenAI supports their communication strategies, enhances productivity, and bolsters their efforts in responding to workplace discrimination rooted in misogynoir , the distinct, intersecting form of racism and sexism that specifically targets Black women. At the same time, these tools also reinforce existing power structures and do not address the fundamental need for Black women to show up as their full, authentic selves. We conclude by outlining Black feminist–informed directions that center resistance, survival, and collective world-building beyond hostile tech workplaces.

Research on technology for older people focuses on older people’s experiences–and understandably so. However, the phenomena of othering, or seeing this group as different and worse off, is a persistent problem. In this project, we turned inwards through an 8-month collaborative autoethnography to understand our own experiences with technology issues and supporting others in technology use. We found that each member of our mixed age group faced pervasive and burdensome technology issues and recognized that some of the burden is associated with the evolution of technology tools. Our work contributes an expanded understanding of aging as a sociotechnical process and identifies counternarratives to implicit assumptions we held as HCI researchers working with older people. Our research also shows how reflexive positional methods can surface often unexamined experiences with technology issues and aging among researchers.

Community-based partnerships are essential to creating inclusive and equitable technologies and design practices. Though recent scholarship in HCI focuses on equitable design practices, there is less focus on understanding the experiences of community-based nonprofit organizations (CBOs) when partnering with technology companies. In this paper, we focus on understanding the perspectives of CBOs by answering the following research question: What are the experiences of CBOs that have collaborated with technology companies? Through a series of design workshops with 18 participants who work at community-based nonprofits that have collaborated with technology firms, we identified four elements of community-industry collaborations that collectively shape the overall experience: divergences in cultural and organizational norms, ''setting the table,'' project relationship dynamics, and affective qualities. We conclude by discussing the power structures that impact community-industry collaboration and suggest reflective practices to guide equitable collaborations between CBOs and tech companies.

There has been growing attention in HCI to the potential for community-based participatory research (CBPR) to cause harm to community partners. Extractive research is when researchers take ''data'' (i.e., stories, knowledge) and other resources (e.g., time, labor) from communities but provide little in return. Scholars have examined collaboration practices, but this work has yet to focus on data analysis. We (academic and community researchers) explore the benefits, challenges, and power dynamics involved in collaborative analysis. We reflected on our process to co-analyze workshop data from a community-led initiative through member-checking interviews and a duo ethnography. In this paper, we detail the co-analysis approach we used and examine how structural power can incentivize extractive research practices. We pose that co-analyzing data according to community-defined questions can mitigate harm and advance community partners' goals.

Conversational voice assistants are often imbued with personality and human-like characteristics (e.g., gender). While researchers have begun to examine and design for the downstream societal impacts of voice assistants encoding characteristics such as gender, we know little about other human-like characteristics such as age that are encoded in an artificial, yet, anthropomorphic voice. As older adults continue to adopt voice assistants, we brought older adults into an activity to customize human-like characteristics for their voice assistant. Our findings reveal the different stereotypes and assumptions individuals associated with voice assistant characteristics (e.g., age, gender, race). We also describe individuals' motivations behind customizing or not customizing these characteristics. We discuss how biases get encoded through our design process, marginalizing older adults and other non-dominant user groups and call for a need to examine the systemic, yet unspoken, power structures encoded in anthropomorphic technologies.