Specialisation 1 Specialisation 2 Specialisation 3 Specialisation 4
Digital Technology in Society
The Specialization is focused on the study of the various intersections and the co-shaping of digital technologies and infrastructures and social, political, cultural, legal and economic circumstances. Of particular interest is the cluster of technologies that include Artificial Intelligence (AI), Robotics and Automation, Big Data, Social Media and The Internet of Things (IoT). The Specialization covers the whole range of the social practices, cultural patterns and institutional arrangements emerging with these technologies, from science/technology/medicine to market/business and state/government contexts. It is designed so as to support STS research in fields like digital infrastructure studies, critical data and algorithm studies. The following topics are included:
-Europeanization and Digitalization
-Digital Economy, Digital Entrepreneurship, Digital Business
-Digitalization and Labor/Work, Human Rights, Gender, Race, Disability, Surveillance
-Digitalization of Culture, Digital Humanities, Digital Heritage, Digital Museums, Digital Art
-Digitalization and Borders, Digitalization and Migration
-Ethics of Digitalization (e.g. AI Ethics, Big Data Ethics, Ethics and Robotics)
-History of Digitalization
The Specialization provides an organized introduction to social practices, cultural patterns and institutional arrangements emerging with digital technologies and infrastructures, from various STS perspectives. Students will be introduced to the main STS theories, concepts and methodologies involved in studying and intervening in processes of digital transformations.
Through the Specialization they will become able to systematically:
-retrieve the social side of digitalization initiatives
-identify the economic, political and ideological dimensions of drives to digitalization
-elaborate on social limits to digitalization
The articles listed below and selected chapters from the books listed below:
Alper, M. (2017). Giving Voice: Mobile Communication, Disability, and Inequality. The MIT Press, Cambridge, Massachusetts.
Amelung, N., Granja, R. & Marchado, H. (2020). Modes of Bio-Bordering: The Hidden (Dis)integration of Europe. Palgrave.
Aronova, I., von Oertzen, C. & Sepkoski, D. (2017). Introduction: Historicizing Big Data. Osiris, 32, 1-17.
Blume, S (2012). What can the study of science and technology tell us about disability?. In Nick Watson, Alan Roulstone and Carol Thomas (eds.): Routledge Handbook of Disability Studies, Routledge, London & New York, 348-359.
Broussard, M. (2018). Artificial Unintelligence: How Computers Misunderstand the World. The MIT Press, Cambridge, Massachusetts.
Cheney-Lippold, J. (2017). Cheney-Lippold, J: We Are Data. Combined Academic Publ.
Collins, H. (2018). Artifictional Intelligence: Against Humanity’s Surrender to Computers. Polity Press, UK.
Dijstelbloem, H. & Meijer, A. (editors). (2011). Migration and the New Technological Borders of Europe, Palgrave Macmillan, UK.
Dobson, J. (2019). Critical Digital Humanities: The Search for a Methodology. University of Illinois Press, Chicago, Illinois.
Dourish, P. (2016). Algorithms and their others: Algorithmic culture in context. Big Data & Society, 3(2), 2053951716665128. https://doi.org/10.1177/2053951716665128
Eubanks, V. (2019). Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. Picador, New York, New York.
Eubanks, V. (2011). Digital Dead End: Fighting for Social Justice in the Information Age. The MIT Press, Cambridge, Massachusetts.
Fuller, M., & Goffey, A. (2012). Digital Infrastructures and the Machinery of Topological Abstraction. Theory, Culture & Society, 29(4–5), 311–333. https://doi.org/10.1177/0263276412450466
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), 2053951716679677. https://doi.org/10.1177/2053951716679677
Heymann, M., Gramelsberger, G. & Mahony, M. (editors). (2017). Cultures of Prediction in Atmospheric and Climate Science: Epistemic and Cultural Shifts in Computer-based Modelling and Simulation. Routledge, London, UK.
Jasanoff, S. (2017). Virtual, visible, and actionable: Data assemblages and the sightlines of justice. Big Data & Society, 4(2), 2053951717724477. https://doi.org/10.1177/2053951717724477
Kitchin, R., & Lauriault, T. (2018). Towards Critical Data Studies: Charting and Unpacking Data Assemblages and Their Work. In J. Thatcher, A. Shears, & J. Eckert (Eds.), Thinking Big Data in Geography: New Regimes, New Research (pp. 3–20). University of Nebraska Press.
Misa, T. (editor). (2010). Gender Codes: Why Women Are Leaving Computing. Wiley – IEEE Computer Society, Hoboken, New Jersey.
Noble, S. U. (2018). Algorithms of Oppression: How Search Engines Reinforce Racism. Combined Academic Publ.
Pelizza, A. (2016). Developing the Vectorial Glance: Infrastructural Inversion for the New Agenda on Government Information Systems. Science, Technology, & Human Values, 41(2), 298–321. https://doi.org/10/f78wjq
Plantin, J.-C., Lagoze, C., Edwards, P. N., & Sandvig, C. (2018). Infrastructure studies meet platform studies in the age of Google and Facebook. New Media & Society, 20(1), 293–310.
Ruppert, E., Isin, E., & Bigo, D. (2017). Data politics. Big Data & Society, 4(2), 205395171771774. https://doi.org/10.1177/2053951717717749
Straube, T. (2016). Stacked spaces: Mapping digital infrastructures. Big Data & Society, 3(2), 2053951716642456. https://doi.org/10.1177/2053951716642456
Tatnall, A. (editor). (2012). Reflections on the History of Computing: Preserving Memories and Sharing Stories. Springer, London, UK.
Tympas, A. (2020). From the Display of a Digital-Masculine Machine to the Concealed Analog-Feminine Labour: The Passage from the History of Technology to Labour and Gender History. Historein 19.1. (https://ejournals.epublishing.ekt.gr/pfiles/journals/14/editor-uploads/issues/1165/main1165.html?1=1165&2=19134)
Wachter-Boetcher, S. (2017). Technically Wrong: Sexist Apps, Biased Algorithms, and Other Threats of Toxic Tech. WW Norton & Co.
Ziewitz, M. (2016). Governing Algorithms Myth, Mess, and Methods. Science, Technology & Human Values, 41(1), 3–16. https://doi.org/10.1177/0162243915608948
Zuboff, S. (2019). The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. Profile Books, London, UK.
ESST student who choose this Specialization are following a course that combines an intensive one-month introduction to the overall Specialization (to run from mid-February to mid-March) and semester-long lectures/discussions on representative Specialization themes. Depending on the focus of their thesis, students may be hosted by the Interdepartmental Graduate Program ‘Science, Technology, Society—Science and Technology Studies’ (National and Kapodistrian University of Athens, Athens, Greece) or the European New School of Digital Studies (European University Viadrina, Frankfurt, Oder, Germany).
For a sample of recent ESST theses that are relevant to this Specialization, see:
Thomas Verra, Issues alongside the Integration of Artificial Intelligence and Big Data into CRISPR/Cas9 gene editing, 2020.
Kyriaki Giagkousi, Gender and computing algorithms: Τhe case of ‘stable matching’, 2020.
Georgiana Kotsou, Discourses on the Emergence of Big Data Technologies in the Greek Medical Press Approach, 2019.
Anastasia Stoli, Picturing Big Data in media, 2019.
Aristotle Tympas (specialization coordinator) (PhD, Georgia Tech, 2001), Professor (AI, algorithms, big data; environmental degradation and digitalization; gender)
Katerina Vlantoni (PhD, NKUA, 2016), Postdoctoral Fellow (biomedicalization and digitalization)
Manolis Simos (PhD, University of Cambridge, 2018), Postdoctoral Fellow (ethics of technology; science, technology and literature)
Giorgos Zoukas (PhD, University of Edinburgh, 2019), Postdoctoral Fellow (science communication)
Nikos Karabekios (PhD, NKUA, 2011) Affiliated Researcher, Head of the Metrics and Innovation Unit, National Documentation Center (digitalization metrics/indicators)
Olga Lafazani (PhD, Harokopeio University, 2014), Affiliated Researcher (gender; migration; geography)
Prof. Dr. Jürgen Neyer, Vice President for International Affairs, European New School of Digital Studies (ENS), European University Viadrina, Germany
Prof. Dr. Jan-Hendrik Passoth, Professor of Sociology of Technology, New School of Digital Studies (ENS), European University Viadrina, Germany
Dr. Vasilis Galis, Associate Professor, Technologies in Practice Group, ITU Copenhagen, Denmark
Dr. Nina Amelung, Communication Sciences Department, Communication and Society Research Centre (CECS), University of Minho, Portugal
Aristotle Tympas (firstname.lastname@example.org)