Theories/Practice of Big Data [for Social Scientists]

Sociology 290: Theories/Practice of Big Data

Thursdays 9:10-11:40

SSB 101

Prof. Juan Pablo Pardo-Guerra

This class introduces students to critical perspectives on data, machine learning, and algorithms from and for the social sciences. In the past SOC 290 was organized as a workshop: students worked in groups on original research projects; their work was organized into three thematic units that covered core competencies of data scientists.

This year, I’ve decided to introduce some changes. Most of the students registered for the class have expertise in data science (the vast majority of students are from CS), so it would be poor use of time and resources to learn how to code (in all likelihood, you are already experienced coders). The added value of this year’s course is not in teaching specific and mysterious ways through which you can study ‘society’ through data but rather to get you to reflect about the assumptions, practices, and implications of working with big data. This is more important than knowing how to code: recent public controversies around big data are all the result of coding practices that didn’t take into account how algorithms and data structures reproduce biases, inequalities, and stereotypes. In this class, we will learn how to reflect on these and other issues.

SOC 290 is also practical, and as part of the course students are required to develop a research project that looks at something ‘social’. You can analyze Twitter, Facebook, Jstor, or any other digital source.

WEEK 0 –First meeting

UNIT 1 – ON THE PRINCIPLES OF BIG DATA —————————–

WEEK 1 – What is data?

Boellstorff, Tom (2013) “Making big data, in theory” First Monday, 18(10)

http://firstmonday.org/ojs/index.php/fm/article/view/4869/3750

DOI: 10.5210/fm.v18i10.4869.

Desroisieres, Alain (1991) “How to Make Things Which Hold Together: Social Science, Statistics and the State” in Peter Wagner et. al. Discourses on Society: The Shaping of the Social Science Disciplines Springer

Rosenberg, Daniel (2013) ‘Data before the fact’ in Gitelman, Lisa, “Raw Data is an oxymoron”, MIT Press: Cambridge, MA.

Rieder, Bernard (2012) “What is PageRank? A historical and conceptual investigation of a recursive status index” Computational Culture Available at: http://computationalculture.net/article/what_is_in_pagerank

Newitz, A. (2017) “The secret lives of Google raters” At: https://arstechnica.com/features/2017/04/the-secret-lives-of-google-raters

WEEK 2 – Why does big data matter?

Brayne, Sarah. (2017) “Big data surveillance: the case of policing” American Sociological Review

Introna, Lucas. (2015) “Algorithms, governance and governamentality” Science, Technology and human Values

Fourcade, Marion and Kieran Healy (2013) “Classification Situations: Life-chances in the Neoliberal Era.” Accounting, Organizations, and Society 38: 559–572.

boyd, danah and Kate Crawford (2012) “Critical questions for big data: provocations for a cultural, technological, and scholarly phenomenon”, Information, Communication & Society 15(5)
DOI: 10.1080/1369118X.2012.678878

UNIT 2 – METHODS —————————–

WEEK 3 – Counting things

Legewie, J., & Schaeffer, M. (2016). Contested Boundaries: Explaining Where Ethnoracial Diversity Provokes Neighborhood Conflict. American Journal of Sociology, 122(1), 125-161.

Leung, M. D. (2014). Dilettante or Renaissance Person? How the Order of Job Experiences Affects Hiring in an External Labor Market 1. American Sociological Review, 79(1), 136-158.

WEEK 4 – Looking for Relatives

Knigge, A., Maas, I., & van Leeuwen, M. H. (2014). Sources of sibling (dis) similarity: Total family impact on status variation in the Netherlands in the nineteenth century. American journal of sociology, 120(3), 908-948.

Lin, K. H., & Lundquist, J. (2013). Mate selection in cyberspace: The intersection of race, gender, and education. American Journal of Sociology, 119(1), 183-215.

Curington, Celeste Vaughan, Ken-Hou Lin, and Jennifer Hickes Lundquist. “Positioning multiraciality in cyberspace: Treatment of multiracial daters in an online dating website.” American Sociological Review 80, no. 4 (2015): 764-788.

WEEK 5 – Doing things with words

Bail, C. A. (2012). The fringe effect: Civil society organizations and the evolution of media discourse about Islam since the September 11th attacks. American Sociological Review, 77(6), 855-879.

Fligstein, N., Stuart Brundage, J., & Schultz, M. (2017). Seeing Like the Fed: Culture, Cognition, and Framing in the Failure to Anticipate the Financial Crisis of 2008. American Sociological Review

WEEK 6 – Project Workshop

Healy, K., & Moody, J. (2014). Data visualization in sociology. Annual review of sociology, 40, 105-128.

Healy, K. (2017) Data visualization for social science at http://socviz.co/

UNIT 3 – WHAT NOT TO DO ———————

WEEK 7 – Case 1: The morality of self-driving cars & large-scale experiments

See: moralmachine.mit.edu

WEEK 8 – Case 2: The Kosinski affair

Kosinski, M., & Wang, Y. (2017). Deep neural networks are more accurate than humans at detecting sexual orientation from facial images.

The Guardian “New AI can guess whether you’re gay or straight from a photograph” at https://www.theguardian.com/technology/2017/sep/07/new-artificial-intelligence-can-tell-whether-youre-gay-or-straight-from-a-photograph

WEEK 9 – Case 3: Biased algorithms

Knight, W. (2017) “Biased algorithms are everywhere, and no-one seems to care”. MIT Technology Review. https://www.technologyreview.com/s/608248/biased-algorithms-are-everywhere-and-no-one-seems-to-care/

Spielkamp, M. (2017) “Inspecting algorithms for bias” in MIT Technology Review https://www.technologyreview.com/s/607955/inspecting-algorithms-for-bias/

Cain Miller, C. (2015) “When algorithms discriminate” NY Times https://www.nytimes.com/2015/07/10/upshot/when-algorithms-discriminate.html

WEEK 10 – Presentations

 

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