Day Ten: Labor

SDS 237: Data Ethnography

Lindsay Poirier

You are scheduled to interview an AI “ghost worker.” What is one question you might ask them to deepen understanding of the social configurations of their work?

Turn to a neighbor and discuss:

  • How would the response to this question deepen understanding of the cultural underpinnings of a data infrastructure?

Reading Discussion

How is labor divided across different social demographics?

  • Care work: labor that involves caring for others or care for our environments
  • Often a form of gendered division of labor
  • In what ways might data labor be divided across gender/race/class? Who cares for data?

a species activity that includes everything we do to maintain, continue, and repair our world so that we may live in it as well as possible. That world includes our bodies, our selves, and our environment, all of which we seek to interweave in a complex, life-sustaining web. (Fisher & Tronto, 1990, p. 34)

How do workers “do” their work?

  • How do individuals acquire the knowledge to perform their work?
  • How do individual and organizational values shape the way they approach their work?
  • How do individuals improvise when certain demands of their work become impossible or untenable?

How do major social and economic shifts reconfigure the way labor gets organized and valued?

  • Fordism: a system of mass producing goods in the early 20th century that involves workers engaging rote, standardized tasks (often in assembly line)
  • Post-fordism: a shift in systems of mass production that prioritizes consumer choice and individualized goods
    • Seeks to make production more flexible and culturally responsive
    • Labor conditions became more individualized and specialized, while also more disposable
  • Neoliberalism: an economic philosophy and policy model emerging in the 1970s aimed at securing free-market capitalism
    • Involves deregulation of corporate activities, privitization of services and goods, and the off-shoring of jobs

What does AI data work look like today?

  • Rise of the gig economy
  • Flexibility and precarity of labor practices
  • Minimal protections and organized labor structures for data workers
  • Entrepreneurialism and self-management valued amongst laborers
  • Specialized consultants prioritized over having internal expertise

Wednesdays’s Reading

  • Considering “what is data science” in universities
  • Interested in the work that people do to draw the boundaries of data science
  • Outlines two visions of what kind of discipline data science is
  • Transdiscipline - Boundary work by university leaders
  • Extradiscipline - Boundary work by the people actually doing data science
  • Pay attention to how these two different visions of data science get enacted!