Numar de credite: 6

Cod: AME0153

Predare: curs 2h, seminar 1 h

Limba de predare: Română

Tip: opţional, semestru 3, Master Muncă și transformări sociale

Lectures  

Topics and mandatory readings:

Topic 1

What is Big Data? Problematics of the Field. 

Mayer-Schönberger, Victor, and Kenneth Cukier. 2013. Big Data, A Revolution That Will Transform How We Live, Work, and Think. Boston, MA: Houghton Mifflin Harcourt, Chapter 1 (Now), Chapter 6 (Value).

Topic 2

Materiality of Big Data. Collection, Storage, Access, Investigation, Distribution, Security, Transfer. 

Mayer-Schönberger & Kenneth Cukier 2013, Chapter 7 (Implications).

Kate Crawford, Vladan Joler. 2018. Anatomy of an AI System. The Amazon Echo as an anatomical map of human labor, data and planetary resources. https://anatomyof.ai/

Topic 3

Science, Knowledge and Big Data. Examples from various disciplines and fields of practice (Medicine, Linguistics etc.).

Topic 4

Big Data and Artificial Intelligence. Ethics of AI.

Neural networks-deep learning– explanatory video

Coeckelbergh, M. 2020. AI Ethics. Cambridge, MA: MIT Press.

Topic 5

Big Data and the State. The Question of Government. The Question of Security. Policing. Surveillance. Social scoring.

Aradau, Claudia and Tobias Blanke. 2015. “The (Big) Data-security assemblage: Knowledge and critique”. Big Data and Society. 2015: 1-12. 

Topic 6

Big Data in Economy and Finance. Credit Scoring and Big Data. 

Poon, Martha. 2009. ‘‘From New Deal Institutions to Capital Markets: Commercial Consumer Risk Scores and the Making of Subprime Mortgage Finance.’’ Accounting, Organization and Society 34 (5): 654-74.

Topic 7

Big Data and Personhood. The Politics of measurement. Cuantification, Evaluation, Classification. Algorithmic shaping of the Subject. 

Mayer-Schönberger & Kenneth Cukier 2013, Chapter 8 (Risks), and Chapter 9 (Control).

Topic 8

Data Analytics and Algorithms. Epistemological Implications.

Lucy Resnyansky. 2019. Conceptual frameworks for social and cultural Big Data analytics: Answering the epistemological challenge. Big Data and Society, January–June 2019: 1–12.

Mayer-Schönberger & Kenneth Cukier 2013, Chapter 2 (More), Chapter 3 (Messy), Chapter 4 (Correlation).

Topic 9

Big Data and the Future of Capitalism. Self-regulated Distributed Systems, Algorithms, Fuzzy Control.

Jacob Silverman. 2014. „The Crowdsourcing Scam. Why do you deceive yourself?”. The Baffler 26. http://thebaffler.com/salvos/crowdsourcing-scam

Topic 10

Elected topic: Commercial DNA Testing and BD.

Alondra Nelson. 2016. The Social Life of DNA: Race, Reparations, and Reconciliation After the Genome. New York: Beacon Press.

Seminars will follow course topics

Selected Supplementary literature:

Acemoglu, Daron, Simon Johnson. 2023. Power and Progress: Our Thousand-Year Struggle Over Technology and Prosperity. Hachette Book Group.

Anderson, Chris. 2008. The End of Theory: The Data Deluge Makes the Scientific Method Obsolete. Wired. Science section. 06.23.2008

https://www.wired.com/2008/06/pb-theory/

Dow Schull, Natasha. 2014. Addiction by Design: Machine Gambling in Las Vegas. Princeton, NJ: Princeton University Press.

Eggers, Dave. 2013. The Circle. New York: Alfred A. Knopf.

Eubanks, Virginia. 2018. Automating Inequality: How High-Tech Tools Profile, Police and Punish the Poor. New York: St Martin’s Press.

European Economic and Social Committee. 2017. “The Ethics of Big Data: Balancing Economic Benefits and Ethical Questions of Big Data in the EU Policy Context.” 22 Feb. 2017.

https://www.eesc.europa.eu/en/our-work/publications-other-work/publications/ethics-big-data

EU AI Act 2024 (12 July 2024) https://artificialintelligenceact.eu/

Federal Trade Commission. 2016. Big Data: A Tool for Inclusion or Exclusion? Washington, DC: Federal Trade Commission.

Halpern, Orit. 2014. Beautiful Data. A History of Vision and Reason since 1945. Duke University Press.

Howard, Philip N. 2015. Pax Technica: How the Internet of Things May Set us Free or Lock us up. New Haven, CT: Yale University Press.

Kitchin, Rob. “Big Data, New Epistemologies and Paradigm Shifts.” Big Data & Society, (April 2014). https://doi.org/10.1177/2053951714528481.

Latour, Bruno, and Steve Woolgar. (1979) 1986. Laboratory Life: The Construction of Scientific Facts. Princeton, NJ: Princeton University Press.

O’Neil, Cathy. 2017. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Broadway Books.

Zuboff, Shoshanna. 2019. The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. Campus, 2018; Public Affairs, 2019.

4 reaction notes (20%), during the semester – each reaction note 0.5 points + a research report on a topic selected by the instructor (80%), deadline 26 January 2025. Grade 5 to pass the course.

General objective of the course

The course will help us understand how the production, transaction, distribution, and use of big data are organised and governed, and their consequences in the social world.

Sociologically, the political economy of big data investigates the politics and economic forms of the production of value from big data. They are regimes where power and influence structures rest on using, monetizing, financializing, and extracting value of some sort from big data.

The course aims to open horizons of understanding of the transformations brought by the capacity to produce and exploit big data, and develop critical reflection on how they impact social world.

Specific objective of the course

Students will be able to identify modes of collection, storage, use of big data in various spaces of present social life such as education, state and security, citizenship, the economic realm – finances and banks, virtual space, self-generated cooperation networks.