Lecture 1

excerpts to discuss:

• Wallach, Hannah. Big data, machine learning, and the social sciences: Fairness, accountability, and transparency. Medium. Retrieved December 20, 2014, from https://medium.com/@hannawallach/big-data-machine-learning-and-the-social-sciences-927a8e20460d

  • "uncomfortable": why?
  • types of analyses:
    • when might we describe vs predict?
    • who might be more interested in one than the other?
    • how might these be used?

• boyd, danah, and Kate Crawford. 2012. "Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon." Information, Communication & Society 15.5: 662-679. http://www.tandfonline.com/doi/abs/10.1080/1369118X.2012.678878

  • why do we strive for "objectivity"? (the answer should not contain the word "truth")
  • think though the sources of subjectivity in the chronology of you relationship with a dataset, e.g.,
    • how the data were generated
    • your mental model of this