Data science has experienced rapid growth in recent years, driven largely by the progress in machine learning. This development has opened up new opportunities in a wide range of social, scientific, and technological fields. However, it has become increasingly clear that focusing solely on the statistical and numerical aspects of data science often overlooks social nuances and ethical considerations. The field of Human-Centered Data Science (HCDS) is emerging to fill this gap, combining elements of human-computer interaction, social science, statistics, and computational techniques.
HCDS emphasizes the fundamental principles of data science and its human implications. These include research ethics, privacy, legal frameworks, algorithmic bias, transparency, fairness, accountability, data provenance, reproducibility, user experience design, human computation, and the societal impact of data science.
By the end of this course, students will be expected to
- Apply human-centered design methods to data science practice, taking into account ethical concerns and privacy requirements.
- Construct a reproducible data science workflow.
- Understand and differentiate key terms such as bias, fairness, accountability, transparency, and interpretability.
- Apply measures, techniques, and frameworks to make their data science results interpretable in the context of human-centered explainable AI (HC-XAI).
- Enhance data science workflows with qualitative research approaches.
- Be aware of the existing measures, techniques, and approaches that help to reflect on data science practices.
Students will not only understand the core concepts, theories, and practices of HCDS, but also the multiple perspectives from which data can be collected and processed. In addition, students will gain insight into the potential societal implications of current technological advances. This course aims to equip students with the ability to apply data science techniques in a mindful and conscientious manner, taking into account human and societal contexts, resulting in more ethical, inclusive, and meaningful data-driven solutions.
Here you can find our Code of Conduct.
Literature
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).