Euro CSS

The European Symposium Series on Societal Challenges in Computational Social Science (2017-2019) is completed. We would like to thank all speakers, participants, reviewers and everyone who supported these events - especially the Volkswagen Foundation for making this entire series possible. We hope to announce new events for Computational Social Science in the future. (Your #eurocss team)

London 2017 - Inequality and Imbalance

Last year we successfully organized the first European Symposium on Societal Challenges in Computational Social Science with the emphasis on Inequality and Imbalance in the dynamic London. Read more about it here.

Cologne 2018 - Bias and Discrimination

This year we are organizing the second European Symposium on Societal Challenges in Computational Social Science with the emphasis on Bias and Discrimination in Cologne. Find more about it here.

Zurich 2019 - Polarization and Radicalization

Next year, the third European Symposium on Societal Challenges in Computational Social Science with the emphasis on Polarization and Radicalization will be organized in Zurich. Find more about it here.

About the Series

This is a series of three symposia that discuss societal challenges in computational social sciences. Each year, the symposium has a focus on a specific subtopic: “Inequality and Imbalance” (London, 2017), “Bias and Discrimination” (Cologne, 2018) and “Polarization and Radicalization” (Zurich, 2019).

With these three events we provide a platform to address one of the most pressing challenges in today’s digital society: understanding the role that digital technologies, the Web, and the algorithms used therein play in the mediation and creation of inequalities, discrimination and polarization.

By addressing inequality as the topical issue for the symposium series we intend to explore how CSS can contribute to opening up new ways of thinking about, of measuring, detecting and coping with social inequality, discrimination, and polarization. We will discuss how divides and inequalities are proliferated in digital society, how social cleavages can be observed via web data, how the organizational structure of the web itself generates biases and inequality, and how, in contrast, algorithms and computational tools might help to reduce discrimination and inequality. We will also investigate how bias and unequal social structures foster political tension and polarization, including issues of radicalization and hate.