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.

[Databite No. 157] Recognition | Generative AI's Labor Impacts

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The Formalization of Social Precarities podcast explores platformization from the point of view of precarious gig workers in the Majority World. This conversation was moderated by Aiha Nguyen and Murali Shanmugavelan featuring the voices of Ambika Tandon, Ludmilla Costhek Abílio, and Ananya Raihan. You will also be hearing the experience of two platform workers interviewed for this project: Fatema Begum from Bangladesh and Nicolas Sauza from Brazil. Their voices are narrated in English by Data & Society staff members Iretiolu Akinrinade and Rigoberto Lara Guzmán, respectively.

This podcast was edited by Sam Grant.

[Podcast] The Formalization of Social Precarities

Data & Society’s report, Doing the Work: Therapeutic Labor, Teletherapy, and the Platformization of Mental Health Care, written by Livia Garofalo, explores how these new arrangements of therapeutic labor are affecting how therapists provide care and make a living in the US. By focusing on the experiences of providers who practice teletherapy and work for digital platforms, our research examines the fundamental tensions that emerge when a profession rooted in clinical expertise, licensing, and training standards meets the dynamics of platformization, productivity incentives, and algorithmic management. 

In this conversation, we reflected on how technology is changing the conditions of how therapists do their work, on the consequences for the present and future of therapeutic labor, and on how this might be changing our understanding of therapy itself.

[Databite 159] Doing the Work: Therapeutic Labor, Teletherapy, and the Platformization of Mental Health Care

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.

[Databite No. 158] Adaptation | Generative AI's Labor Impacts

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.

What's Trust Got To Do With It? | 'Trust Issues' Workshop Public Panel

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.

[Databite No. 157] Recognition | Generative AI's Labor Impacts

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