In recent years, the field of data science has developed rapidly, primarily due to advances in machine learning. This development has opened up new possibilities in a variety of social, scientific, and technological areas. However, based on the experiences of the last few years, it is becoming increasingly clear that a focus on purely statistical and numerical aspects in data science neither captures social nuances nor takes ethical criteria into account. The research area of 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 the fundamental principles of Data Science and their impact on people, including research ethics, privacy, legal frameworks, algorithmic bias, transparency, fairness and accountability, as well as data provenance, curation, preservation and reproducibility, user experience design and (re)search of large data sets, human computing, and, in addition, effective oral, written and visual scientific communication and the societal impact of data science.

At the end of this course, students understand the main concepts, theories, practices, and various perspectives from which data can be collected and manipulated. In addition, students are able to recognize the impact of current technological developments on society.

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. CSCW Companion, New York, ACM (pp. 529–535).

Baumer, E. P. (2017). Toward human-centered algorithm design:. Big Data & Society, 4(2).

Kogan, M., Halfaker, A., Guha, S., Aragon, C., Muller, M., & Geiger, S. (2020). Mapping Out Human-Centered Data Science: Methods, Approaches, and Best Practices. ACM International Conference on Supporting Group Work (pp. 151-156).