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Instructors: Quoc-Tan Tran
Event type:
Intermediate seminar
Displayed in timetable as:
Data ethics: Concepts, approaches and cases
Hours per week:
2
Credits:
7,0
Language of instruction:
English
Min. | Max. participants:
- | -
Comments/contents:
What constitutes good data? How is data prepared, and who decides what counts as data? Who is responsible for producing good data work? In the age of rapidly expanding data science and computing capacities, the social effects of data technology will not become apparent until widespread adoption of the technology has occurred. Once data technology is widely used, regulations might be less effective at mitigating its negative consequences. This is the case with data science, which requires a new mindset and methods of doing research, together with new digital tools. Data specialists face ethical challenges while managing data and designing procedures as they attempt to create accessible and reusable knowledge. This seminar will introduce the societal impact of data science and such ethical concerns as privacy rights and data validity. In examining the everyday work of data practitioners, we look at the emerging challenges that influence the design and function of data science products.
Throughout this seminar, we articulate our views on the diverse themes that underpin the ethical debates in the age of big data: data economies, data asymmetries, algorithmic bias, digital language divide, and indigenous perspectives in global knowledge regimes.
This course is open to both bachelor's and master's degree students.
Literature:
Edwards, P. N. (2017). Knowledge Infrastructures for the Anthropocene. The Anthropocene Review, 4(1), 34–43.
Stiegler, B. (2016). The Future of Work. Automatic society: Vol. 1. Cambridge: Polity Press.
Thylstrup, N. B. (2018). The politics of mass digitization. Cambridge, Massachusetts: The MIT Press.
Winner, L. (1977). Autonomous technology: Technics-out-of-control as a theme in political thought. Cambridge, Mass.: MIT Press.
Wylie, C. D. (2021). Preparing Dinosaurs: The Work behind the Scenes. Cambridge, MA: The MIT Press.
Zwitter, A. (2016). The Network Effect on Ethics in the Big Data Age. In A. Bunnik (Ed.), Big data challenges: Society, security, innovation and ethics (pp. 23–34). London, England: Palgrave Macmillan.
Module abbreviation:
56-105 (5 LP)
BA HF/NF: EKW (fsb13-14)- HF-M3, NF-M3, NF-M5, M11, SG, WB-Kultur;
MA: M7/WB-FV fachliche Vertiefung
56-105 (7 LP) mit MAP
BA HF/NF: EKW (fsb13-14)- HF-M3, NF-M3, HF-M5, NF-M5
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