Day One: Introductions

SDS 237: Data Ethnography

Lindsay Poirier
Statistical & Data Sciences, Smith College

Fall 2023

Develop a metric that gives me some insight into your summer.

What is ethnography?

  • study of human culture and social relations
  • involves interactions and observations, recording, and analysis
  • data collection methods are predominantly qualitative
  • analysis is predominantly inductive and interpretive

What is data?

What fields of research inform data ethnography?

  • Science and Technology Studies (STS)
    • an interdisciplinary field that examines how science, technology, politics, and culture all co-produce each other
    • STS disciplines include anthropology, sociology, literary studies, political science, economics, and more
    • What might be some examples?
  • Critical Data Studies
    • an interdisciplinary field examining the epistemological, political, social, and ethical aspects of data artifacts, practices, and infrastructures
    • What are some political dimensions of data? Examples?
    • What are some ethical dimensions of data? Examples?
    • Is there a difference?

Who is the professor? Why is an anthropologist teaching data science?

  • Please call me Lindsay (preferred), Professor Poirier, or Dr. Poirier
  • Previously Assistant Professor of Science and Technology Studies at UC Davis
  • Lab Manager at BetaNYC
  • M.S./Ph.D. in Science and Technology Studies from Rensselaer Polytechnic Institute
  • B.S. in Information Technology and Web Science from Rensselaer Polytechnic Institute
  • Dancing, crafting, cooking, re-watching the same TV series over and over again.
  • I have a very spunky dog Madison.

Exercise

  • Ethnographers often collect more data than they know what to do with
  • Write as much as possible about:
    • what people do/why they do it
    • beliefs/values/expertise
    • social structures
    • questions you are left with

Syllabus Review

  • Policies
  • Grading Contract
  • Course Website
  • Perusall
  • Slack

Reading Tuesday

  • In what social contexts and research cultures did the terms Big Data and AI emerge?
  • What are the consequences of perceiving the work and technologies in these domains as “magic”?
  • How does the actual work of AI and Big Data differ from public hype?
  • How do Elish and boyd recommend engaging ethnography in these fields?
  • What is methodological reflexivity, and how might it benefit research into Big Data and AI?