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02/20/2024 | Campus-Meldung

How can we make the data economy fair?

From the distribution of data-economic revenues and the influencing of political opinion-forming to discrimination by algorithms - the data economy faces numerous fairness challenges. The interdisciplinary team of the joint project "Fair digital services: Co-evaluation in the design of data-economic business models (FAIRDIENSTE)", funded by the Federal Ministry of Education and Research, is now publishing a white paper on the privacy platform that explains how companies can reconcile their business models with data-economic fairness.

Photo by Prof. Dr. Jörn Lamla.Image: University of Kassel.
Prof. Dr. Jörn Lamla.

The paper is intended as an analytical, practical tool for the development and implementation of fair business models in the data economy. In addition to practitioners from politics, business and civil society, the paper is also aimed at an interdisciplinary scientific community.

"The practical tools and analytical approaches in the white paper were developed and tested in close cooperation with our practice partners BurdaForward and the Institute for Technology and Journalism. This includes, for example, the extensive analyses of digital journalism at the level of the ecosystem, business models or corporate cultures, but also the methods of participatory business model design or the curation of controversies," says project coordinator Prof. Dr. Jörn Lamla from the University of Kassel. "This was the only way to ensure the practical relevance of the toolbox. It is therefore important for us to expressly thank the practice partners."

Digital services shape - sometimes unnoticed - large parts of private life, which is why it is essential to lay the foundations for digital self-determination here. They also pose challenges with regard to the fair distribution of data-economic revenues or the avoidance of algorithmic discrimination against groups of people. These developments require new approaches to shaping the data economy that go beyond traditional ideas of data and privacy protection.

The aim of the FAIRDIENSTE project is therefore to use sociological and (business) informatics approaches to explore different ways of fairly conveying values that are relevant to business model design in the data economy.

The first step was to develop an analytically sound understanding of fairness that is also capable of uncovering innovative and economically viable alternatives to current business models for concrete data economy design practice. To this end, the researchers in FAIRDIENSTE have tested various approaches to "co-evaluation", which are referred to as offsetting, design and cultivation: Firstly, they investigated the extent to which different values can be translated into an economic language of prices and metrics and fairly offset (offsetting). Secondly, it was worked out how value conflicts can be channeled through technical and regulatory designs (design). Thirdly, it was examined to what extent the public negotiation of value conflicts, e.g. via social media elements, can be promoted in order to contribute to a culture of fairness among users (cultivation).

"Questions of fairness in the data economy affect a large number of stakeholders and are also relevant for companies beyond purely monetary interests," notes co-author Prof. Dr. Thomas Hess from the Institute for Digital Management and New Media at Ludwig-Maximilians-Universität München. "It has become clear that these complex research questions can only be tackled using interdisciplinary approaches and with the close involvement of practitioners. The trusting cooperation within the consortium has proven to be a success factor."


Digital journalism use case: how can its challenges be overcome?

How companies can harmonize their business models with data-economic fairness was examined on the basis of the fairness challenges of digital journalism. On the one hand, digital journalism plays an important role in the democratic formation of opinion and the negotiation of various social values; on the other hand, digital journalism is also strongly characterized by and dependent on data-economic business models. Dynamic changes at the level of business models or infrastructure decisions by large platforms such as Facebook/Meta or Google/Alphabet therefore also have effects on the way in which digital journalism can create framework conditions for democratic opinion-forming. The design of recommendation systems, data and privacy protection requirements, the separation standard for editorial content and advertising or the spread of fake news and hate speech are examples of fairness challenges for digital journalism. However, the considerations on fair business model design contained in the paper are not limited to digital journalism, but should also be applicable to other areas of society.

The white paper will be presented and discussed at the final conference of the FAIRDIENSTE project on February 21/22, 2024 at the Scientific Center for Information Technology Design (ITeG) at the University of Kassel.

The FAIRDIENSTE project is funded by the Federal Ministry of Education and Research from February 2021 to April 2024. FAIRDIENSTE is an interdisciplinary research network that combines the fields of Sociological Theory (Prof. Dr. Jörn Lamla, network coordinator), Participatory IT Design (Prof. Dr. Claude Draude) and Knowledge Processing (Prof. Dr. Gerd Stumme) at the University of Kassel, the Institute for Digital Management and New Media at the Ludwig Maximilian University of Munich (Prof. Dr. Thomas Hess), the company BurdaForward (Dr. Richard Weber and Dr. Alina Hang, Munich) and the Institute for Technology and Journalism e. V. (Miriam Ruhenstroth, Berlin).



Prof. Dr. Jörn Lamla
University of Kassel
Department of Sociological Theory
E-mail: lamla[at]uni-kassel[dot]de