Our societies are facing problems that are more and more complex so that decision making is often helped or even delegated to algorithms (a.k.a. Algorithmic Decision Processes, or ADM). Algorithmic accountability is a field of research that analyzes and explains existing ADM processes for an interdisciplinary audience of lawyers, political scientists, media scientists, journalists, and the general public, analyzes when ADM processes are necessary, develops the measures to quantify the impact ADM has on society, analyzes the mathematical properties and correlations between different methods from machine learning and their quality measures, develops a formalized and structured approach to designs such systems in a transparent, ethic, and accountable way, and finally proposes didactic tools for mainstream public and decision makers concerning the risks and promises of ADM. [more ...]
Network analysis literacy is concerned with when to use which method to analyze networks. The field has seen many methods being proposed in these areas but not many of them have been evaluated with respect to some ground truth. We propose that the network analysis community should agree on benchmark data sets and ground truth or gold standard solutions to show that the proposed algorithms can be tested with regard to their quality. [more ...]
The analysis of complex networks relies mostly on three different approaches: the identification of central nodes, the assignment of nodes to one or more groups of topologically similar other nodes, so-called clusters, and the identification of subgraphs occurring more often than expected by chance. [more ...]
In this cooperation with scientists from the German Cancer Research Center (DKFZ, Heidelberg) we contributed a new method to analyse noisy biological high-throughput data. [more ...]
Cognitive science is one of the upcoming themes in my work group. [more ...]