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Lecture with Exercise: Human-Centered Data Science

(L: 19331101 E: 19331102)

TypeLecture with Exercise
InstructorProf. Dr. Claudia Müller-Birn, Alexa Schlegel
Roomtakes place online and offline
StartNov 02, 2020
endFeb 23, 2021
Time

Lecture Monday 4 am – 6 pm
Exercise Tuesday 4 am – 6 pm

Note

The lecture takes place in a hybrid format; thus, you can attend in presence at Arnimallee 22 (Hörsaal B) or online via live-streaming.

Content

In recent years, data science has developed rapidly, primarily due to the progress in machine learning. This development has opened up new opportunities in a variety of social, scientific, and technological areas. From the experience of recent years, however, it is becoming increasingly clear that the concentration on purely statistical and numerical aspects in data science fails to capture social nuances or take ethical criteria into account. The research area Human-Centered Data Science closes this gap at the intersection of Human-Computer Interaction (HCI), Computer-Supported Cooperative Work (CSCW), Human Computation, and the statistical and numerical techniques of Data Science.

Human-Centered Data Science (HCDS) focuses on fundamental principles of data science and its human implications, including research ethics; data privacy; legal frameworks; algorithmic bias, transparency, fairness, and accountability; data provenance, curation, preservation, and reproducibility; user experience design and research for big data; human computation; effective oral, written, and visual scientific communication; and societal impacts of data science.

At the end of this course, students will understand the main concepts, theories, practices, and different perspectives on which data can be collected and manipulated. Furthermore, students will be able to realize the impact of current technological developments may have on society.

This course curriculum was initially developed by Jonathan T. Morgan, Cecilia Aragon, Os Keyes, and Brock Craft. We have adapted the curriculum for the European context and our specific understanding of the field.

Literature

Aragon, C. M., Hutto, C., Echenique, A., Fiore-Gartland, B., Huang, Y., Kim, J., et al. (2016). Developing a Research Agenda for Human-Centered Data Science. (pp. 529–535). Presented at the CSCW Companion, New York, New York, USA: ACM Press. http://doi.org/10.1145/2818052.2855518

Baumer, E. P. (2017). Toward human-centered algorithm design:. Big Data & Society, 4(2), 205395171771885. http://doi.org/10.1177/2053951717718854

Kogan, M., Halfaker, A., Guha, S., Aragon, C., Muller, M., & Geiger, S. (2020). Mapping Out Human-Centered Data Science: Methods, Approaches, and Best Practices. In Companion of the 2020 ACM International Conference on Supporting Group Work, (pp. 151-156). https://doi.org/10.1145/3323994.3369898