Data & Society Data & Society

Predominant narratives that cast workers as replaceable hide the ways in which workers are actively responding to generative AI. Many build new skills and tools to their advantage while others sabotage, counteract and otherwise circumvent these systems. The relationship workers have with technology is much more dynamic, contested and layered. Narratives that cast workers as replaceable, for example, obscure the active and complex ways that workers are responding to generative AI. While many build new skills and use these tools and systems to their advantage, others sabotage, counteract, and otherwise circumvent them. In this conversation, Livia Garofalo, Jeff Freitas, Quinten Steenhuis and Data & Society host Aiha Nguyen explored the ways that workers reshape their relationship with generative AI tools – and as a result, with work itself.

In a conversation moderated by D&S Principal Researcher Sareeta Amrute, panelists Chelsea Peterson-Salahuddin, Irene Solaiman, and Jason D’Cruz discussed how practitioners, theorists, and community members approach the fraught issue of trust inside and outside institutions. Together, they considered how legacies of racism, dehumanization, refusal, and opacity inform trust — how it operates, and fails to operate, in data-centric spaces. They also discussed trust’s typical framing as a normative construct, as well as the meaning of mistrust and its consequences for vulnerable communities.

Queer people have long been rendered invisible by data systems: survey questions that impose gendered binaries, inquiries that dismiss queer subjects as unimportant or insignificant, and ahistorical erasures of queer life that push queer experiences and knowledge further into the margins.

Yet visibility also comes with risk. Digital and biomedical surveillance, personal data breaches, and privacy concerns arise when indications of queerness, real or otherwise, are present and unprotected in datasets. The threat imposed by interlocking systems of anti-queer violence and oppression seeds movements away from visibility and towards fugitive tactics of refusal — a kind of strategic invisibility.

On February 15 in a conversation moderated by Data & Society Research Analyst Joan Mukogosi, Nikita Shepard and Harris Kornstein discussed this problem of data in/visibility as they explore it in their contributions to Queer Data Studies, an anthology edited by Patrick Keilty featuring essays that examine, from a range of disciplinary approaches, how data impacts queer subjects. Together, Shepard, Kornstein, and Keilty broke down the dichotomy between visibility and opacity of queer subjects in data, and engaged in the generative practice of thinking about data from and through queerness.

In labor parlance, “recognition” is the pathway by which workers become a union. In what other ways can we recognize the value of work — beyond the form it takes? With artists and models finding that generative AI reduces them to their image, their words on a page, notes in a song, and even their measurements, how does this emerging technology diminish the value of workers and their contributions, and how might we recognize it? In this discussion, Enongo Lumumba-Kasongo, Şerife (Sherry) Wong, Sara Ziff, and Aiha Nguyen pry open the black box of generative AI and consider what is lost or appropriated in the process of extraction.

Developers claim generative AI will have sweeping impacts that transform work as we know it, creating new opportunities for workers and unleashing dramatic waves of creativity. But this technology will not affect everyone equally: Societal biases and embedded hierarchies that inform who and what type of work is valuable will also influence how generative AI is rolled out and who benefits from it. In this first conversation of a three part series, John Lopez, Milagros Miceli, and Russell Brandom join Data & Society's Labor Futures Program Director Aiha Nguyen to interrogate these layers of issues around Generative AI technology; consider how it scaffolds on previous economic models, structures, and modes of employment; and explore its impacts on workers across the globe.

When people die, they leave behind not only physical belongings, but digital ones. While they might have had specific wishes for what happens to their online profiles and accounts after their deaths, preserving these digital remains is complex and requires specialized forms of care. Because digital remains are attached to corporate platforms — which have control over what online legacies look like and how long they continue — people’s digital afterlives are not necessarily the ones they would have chosen for themselves.

On November 16, Tamara Kneese and Tonia Sutherland came together for a conversation about their books, which both foreground death as a site for understanding the social values and power dynamics of our contemporary, platform-saturated world. The conversation between these two authors was moderated by Tamara K. Nopper, senior researcher with Data & Society’s Labor Futures program. Together, they explored death as a site of contestation and transformation.