Aarhus University Seal

DATALAB

  • Center for Digital Social Research

About DATALAB


DATALAB – Center for Digital Social Research is an interdisciplinary research center established in 2016 under the School of Communication and Culture at Aarhus University, Denmark. Led by Professor Anja Bechmann, the center conducts forefront research on algorithmic communication platforms and citizens, collectives, and populations in datafied societies. The center focuses on AI-powered platforms, associated socio-technical actors, patterns of agency and influence, and effects on communication flows. 

DATALAB hosts fundamental research projects that are theoretically based, empirically tested, and often including large-scale trace data. Our research has also contributed to informing decisions on policy and regulatory frameworks (e.g. in relation to platforms and AI). The projects at the center utilize a wide range of methods from computational social science often combining learning models with experiments, surveys, digital ethnography, and interviews.   

DATALAB researchers and projects share a vision and fundamental interest in creating novel methods and reinterpreting theories to better understand platforms and the modern techno-social fabric. Our projects provide novel knowledge on algorithmic and data-driven agency and societies with a particular sensitivity towards principles of democracy, human rights, and ethics.


Contact


Anja Bechmann

Center Director
anjabechmann@cc.au.dk
+45 5133 5138





Recent Publications


Sort by: Date | Author | Title

Bilstrup, K. E. K., Kaspersen, M. H., Assent, I., Enni, S. & Petersen, M. G. (2022). From Demo to Design in Teaching Machine Learning. In Proceedings of the 5th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2022 (pp. 2168-2178). Association for Computing Machinery. https://doi.org/10.1145/3531146.3534634
Nyborg, J. C., Pelletier, C. & Assent, I. (2022). Generalized Classification of Satellite Image Time Series with Thermal Positional Encoding. In Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022 (pp. 1391-1401). IEEE. https://doi.org/10.1109/CVPRW56347.2022.00145
Jørgensen, J. R., Nellemann, K. S., Assent, I., Pathak, A. R. & Elster, A. C. (2022). GPU-FAST-PROCLUS: A Fast GPU-parallelized Approach to Projected Clustering. 196-206. Paper presented at EDBT 2022: 24th International Conference on Extending Database Technology. https://doi.org/10.48786/edbt.2022.09

Latest activities



Latest Media Apperances