Digital written exam.
Students are assigned tasks to complete online (on Moodle) in open book format within a two-hour time slot. The net answering time is 90 minutes; 30 minutes are allocated to downloading and uploading the exam form. The exam is to be written in English.
To pass the course, an exam must be taken at the end of term. The exam is based on a set of required readings (approximately, one text per session).
The exam assesses the ability of students to to both accurately describe the lecture contents and readings and provide one's own interpretation thereof. Students are required to independently establish connections between positions brought up throughout the lecture series.
All exam submissions are checked using the University of Vienna's plagiarism software (Turnitin). Answers copy-pasted from the lecture slides or the Internet, copying of texts without the corresponding bibliography or copying from other participants of the same exam will be detected and will be reported.
The independence of the submitted work might be checked in the form of an interview up to four weeks following the examination. Students are required to attend these appointments upon request.
Lecture readings, lecture slides, and oral presentations.
Readings provided via Moodle.
• Latzer, M. & Just, N. (2020). Governance by and of Algorithms on the Internet: Impact and Consequences. Oxford Research Encyclopedia, Communication.
• Floridi, L., Cowls, J., King, T.C., & Taddeo, M. (2020). How to Design AI for Social Good: Seven Essential Factors. Sci. Eng. Ethics 26(3): 1771-1796.
• Fry, H. (2020). Hello World: Being Human in a World of Algorithms. Chapter: “Medicine”. S. 79-112.
• Conrad, P., & C. Stults (2010). “The Internet and the Experience of Illness.” In: Handbook of Medical Sociology, 6th ed., 179–91. Nashville: Vanderbilt University Press.
• Nunn, R. (2020). Discrimination in the Age of Algorithms. In W. Barfield (Ed.), The Cambridge Handbook of the Law of Algorithms (Cambridge Law Handbooks, pp. 182-198). Cambridge: Cambridge University Press.
• Leonelli, S. (2019). Data Governance is Key to Interpretation: Reconceptualizing Data in Data Science. Harvard Data Science Review, 1(1).
• Allhutter, D., Cech, F., Fischer, F., Grill, G. & Mager, A. (2020) “Algorithmic Profiling of Job Seekers in Austria: How Austerity Politics Are Made Effective”, Frontiers in Big Data.
• Manor, I. & Segev, E. (2020). Social Media Mobility: Leveraging Twitter Networks in Online Diplomacy. Global Policy. 11:2. p233-244.
• Gillespie, T. (2013). "The Relevance of Algorithms". In: Media Technologies: Essays on Communication, Materiality, and Society (Gillespie, T. & Boczkowski, P.J. & Foot, K.A., eds.). Cambridge: MIT Press. p. 167 - 193.
• Nowotny, Helga (2021). In AI We Trust: How the COVID-19 Pandemic Pushes us Deeper into Digitalization. Berlin: De Gruyter.