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.
Generative AI has seeped into many corners of our lives, and threatens to upend the economy as we know it, from education to the film industry. How do workers’ encounters with it differ from their experiences with other systems of automation? How are they similar, and how might this help us understand the shape and stakes of this latest technology?
In this three-part Databite series, Data & Society’s Labor Futures program brings together creators, platform workers, call center workers, coders, therapists, and performers for conversations with technologists, researchers, journalists, and economists to complicate the story of generative AI. By centering workers’ experiences and interrogating the relationship between generative AI and underexplored issues of hierarchy, recognition, and adaptation in labor, these interdisciplinary conversations will uncover how new technological systems are impacting worker agency and power.
Learn more about the speakers, series, and references at datasociety.net.