Sociology of Technology

The course aims at providing an in-depth understanding of sustainability issues as they may be connected to specific technological and scientific artifacts and infrastructures. The emphasis will be placed on artifacts and networks of relevance to renewable and conventional energy, as well as computing and telecommunications. The course is designed so as to provide an elaborate understanding of the actors, concepts and policies involved in the definition and pursuit of sustainability. It will introduce to competing definitions of sustainability, debates over the emergence of technical and scientific expertise on sustainability, and comparisons of sustainability policies. 12 face-to-face and 3 online meetings: face-to-face: 23.1.2023-7.2.2023online: 8.2.2023-10.2.2023time: 16:15-17:45room: Collegium Polonicum 18 Beck, S., Forsyth, T., Kohler, P. M., Lahsen, M., & Mahony, M. (2016). The Making of Global Environmental Science and Politics. In U. Felt, R. Fouché, C. A. Miller, & L. Smith-Doerr (Eds.), The Handbook of Science and Technology Studies (4 ed.). MIT Press, 1059-1086. Ensmenger, N., & Slayton, R. (2017). Computing and the Environment: Introducing a Special Issue of Information & Culture. Information & Culture, 52(3), 295-303. Espinoza, M. I., & Aronczyk, M. (2021). Big data for climate action or climate action for big data? Big Data & Society, 8(1), 1-15. Gabrys, J., Pritchard, H., & Barratt, B. (2016). Just good enough data: Figuring data citizenships through air pollution sensing and data stories. Big Data & Society, 3(2), 1-14. Matsumoto, M. (2005). The Uncertain but Crucial Relationship between a “New Energy” Technology and Global Environmental Problems: The Complex Case of the “Sunshine” Project. Social Studies of Science, 35(4), 623–651. Ottinger, G., Barandiaran, J., & Kimura, A. H.  (2016). Environmental Justice:  Knowledge, Technology, and Expertise. In Felt, U., Fouche, R., Miller, C. A., & Smith-Doerr, L. (Eds.), The Handbook of Science and Technology Studies (4th Ed.). Cambridge, MA and London: The MIT Press, 1029-1057 Rankin, W. (2020). The Accuracy Trap: The Values and Meaning of Algorithmic Mapping, from Mineral Extraction to Climate Change. Environment and History, 29(1), 15-43. Johan Schot, W. Edward Steinmueller, Three frames for innovation policy: R&D, systems of innovation and transformative change, Research Policy, Volume 47, Issue 9, 2018, 1554-1567. Sovacool, B. K. (2010). The importance of open and closed styles of energy research. Social Studies of Science, 40(6), 903–930. Aristotle Tympas, ‘Technological black boxing versus ecological reparation: From encased-industrial to open-renewable wind energy’, in Papadopoulos D., Puig de la Bellacasa, M., & Tacchetti, M., (Eds.). Ecological Reparation. Repair, Remediation and Resurgence in Social and Environmental Conflict. Bristol: Bristol University Press, 2023, 362-377.

The course is focused on the co-shaping of digitalization and society. Of particular interest is the cluster of technologies related to Artificial Intelligence, Algorithms, Big Data, Robotics/Automation and Social Media. Special attention is paid to opening the black-box of these technologies so as to retrieve design biases that invisibly lead to work, gender, race and other inequalities. 12 face-to-face and 3 online meetings:face-to-face: 23.1.2023-7.2.2023online: 8.2.2023-10.2.2023 time: 14:15-15:45room: Collegium Polonicum 18 Broussard, M. (2018). Artificial Unintelligence: How Computers Misunderstand the World. The MIT Press, Cambridge, Massachusetts. Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data and Society, 3(1), 1–12. Crawford, K. (2021). Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press. Eubanks, V. (2019). Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. Picador, New York, New York. Noble, S. U. (2018). Algorithms of Oppression: How Search Engines Reinforce Racism. Combined Academic Publ. Halpern, O., Mitchell, R., & Geoghegan, B. D. (2017). The smartness mandate: Notes toward a critique. Grey Room, 68, 106–129. Mager, A. (2012). Algorithmic Ideology: How capitalist society shapes search engines. Information, Communication and Society, 15(5), 769–787. Manolis Simos, Konstantinos Konstantis, Konstantinos Sakalis and Aristotle Tympas, “‘AI Can Be Analogous to Steam Power’ or from the ‘Postindustrial Society’ to the ‘Fourth Industrial Revolution’: An Intellectual History of Artificial Intelligence”, ICON: Journal of the International Committee of the History of Technology, no 1, (2022), 97-116. Strasser, B. J., & Edwards, P. N. (2017). Big Data Is the Answer … But What Is the Question? Osiris, 32(1), 328–345. Aristotle Tympas, Hara Konsta, Theodore Lekkas and Serkan Karas, ‘Constructing Gender and Computing in Advertising Images: Feminine and Masculine Computer Parts’, in Tom Misa (editor), Gender Codes: Women and Men in the Computing Professions, IEEE Press, 2010, 187-209. 

The course offers an introduction into recent approaches to “Digital Sociology”, an emerging field of reflexive and critical accounts focusing on the sociotechnical rearrangements connected to digital infrastructure, platforms, and digital media. After trying to tackle the conceptual and empirical challenges of digital transformations with the classical tools, theories, and methods of sociology in the 1990-2010s, recent approaches have been taking up insights from Science & Technology Studies to contribute to interdisciplinary fields such as Critical Data Studies, Critical Algorithm Studies or FAccT (Fairness, Accountability, Transparency).