Due to the rapid technological progress in digitization, a diverse ecosystem of innovative technologies, platforms and market players has developed – a data economy. The introduction of big data architectures in companies and the associated methods of artificial intelligence (including machine learning) are changing traditional value chains, competitive dynamics and the consumer behavior in markets. This results in three central economic and social challenges:
Data Pooling: Data Access and Data Usage
So far, we have analyzed approaches to individual and collective ownership of data from an economic, legal and technological perspective. We are currently developing this topic further and are studying efficient data access and data usage models. We put these in the context of efficient mechanisms of monetarization of personal data.
Data Analysis and Privacy-by-Design
In the context of data analyses we deal with consumer scoring, privacy and data ethics. By consumer scoring we subsume various methods of classifying (or clustering) data subjects in order, for example, to predict their creditworthiness or profitability. In regards to 'Privacy-by-Design', we also deal with formally verifiable guarantees of privacy, fairness and non-discrimination (data ethics). Through methods of data set depersonalization (e.g. synthetization), a link to data pooling and data sharing models is established.
Artificial Intelligence and Competition
How are the market dynamics in the data economy changing due to the increasing connection of products and services? How does market dominance emerge in digital markets in which personal data play a central role? What effect does the increasing personalization of prices and products have? These are central questions of our third focus, in which we deal with competition and Artificial Intelligence. Here we develop policy innovations for competition and data protection policy in digital markets.