Lecture with Exercise: Human-Centered Data Science
(L: 19331101 E: 19331102)
|Type||Lecture with Exercise|
|Instructor||Prof. Dr. Claudia Müller-Birn|
|Room||Lecture and Exercise: T9/053|
|Start||Apr 19, 2023 | 10:00 AM|
|end||Jul 20, 2023 | 04:00 PM|
Lecture: Wednesday 10 am - 12 pm, Exercise: Thursday 2 pm - 4 pm
Please note that the lecture will start one week later, on Wednesday 26th April.
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 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 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. The course also teaches effective oral, written, and visual scientific communication, as well as skills to reflect on the societal impacts of data science.
At the end of the course, we expect students to be able to
- apply human-centered design methods in the data science practice, including ethical concerns and privacy requirements,
- build reproducible data science workflow,
- know how to differentiate the terms bias, fairness, accountability, transparency, and explanations,
- apply measures, techniques, and frameworks on human-centered explainable AI (XAI),
- responsibly integrate human labor, i.e., crowdsourcing, in data science projects, and
- augment data science workflows by qualitative research approaches.
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 that current technological developments may have on society.
Please note that Jonathan T. Morgan, Cecilia Aragon, Os Keyes, and Brock Craft initially developed this course curriculum. We have adapted the curriculum for the European context and our specific understanding of the field.
Here you can find our Code of Conduct.
Aragon, C., Guha, S., Kogan, M., Muller, M., & Neff, G. (2022). "Human-centered data science: An introduction." MIT Press.
Baumer, Eric PS. “Toward Human-Centered Algorithm Design.” Big Data & Society, 4(2), Dec. 2017. http://doi.org/10.1177/2053951717718854.
Aragon, Cecilia, et al. "Developing a research agenda for human-centered data science." Proceedings of the 19th ACM Conference on Computer Supported Cooperative Work and Social Computing Companion. 2016. http://doi.org/10.1145/2818052.2855518
Kogan, M., Halfaker, A., Guha, S., Aragon, C., Muller, M., & Geiger, S. (2020, January). 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).