Fairness in Information Systems

  • Problem:  


    The increasing emphasis on machine learning applications and the wide-spread use of it are the main factors driving discussions about algorithmic fairness. However, in a sociotechnical system, the algorithmic approach is unable to fully capture the essence of fairness due to its complexity in a socially dynamic interaction. Fairness must account for multiple factors such as context, timeframe, stakeholders. It also frequently requires integrating a multifaceted perspective of information systems (IS), considering the conflicts between the parties understanding of it and accounting for legal, social and technical limitations. The design, development, and testing of fairness in diverse IS contexts are being negatively impacted by the lack of agreement on what constitutes fairness due to the existent tensions between current concepts and scarce research regarding how different conceptualizations collide during IS design.

    With the goal of providing a detailed synthesis of the fairness landscape, the research group studies and conceptualizes fairness from a sociotechnical viewpoint to assist capture the multilateral and complicated nature of fair IS. As algorithms are frequently left to govern our lives, the call for a multidisciplinary approach to fairness definition, operationalization and testing is required.




    • Synthesis of fairness understandings from different research streams.
    • Analyze fairness in a centralized and decentralized IS context.
    • Design, development and testing of the proposed or chosen fairness concept through data/algorithmic/output or human intervention.
    • Contextualization of fairness in multifaceted and socially intricate human-machine interactions (autonomous driving, auction platforms, car sharing or ride sharing platforms, voting platforms etc.).
    • Propose different methods to operationalize fairness goals for a given context.
    • Examine and comprehend fairness using the perspectives of game theory.


    This is an umbrella topic since topics of interest change rapidly. Students are encouraged to propose a topic that is of interest to them within the topic area, as it can be approached from a sociological, economical, technical or purely theoretical angle. The thesis allows you to gain a broad knowledge in fairness and to make a significant contribution towards a scientifically sound fundament.




    Since the suggested topic is broad, a variety of techniques could be used, beginning with a review of the literature and moving on to qualitative methods such as interviews, or quantitative methods like algorithmic interventions, experiment design, or mathematical proofs.




    • Dolata, M., Feuerriegel, S., & Schwabe, G. (2022). A sociotechnical view of algorithmic fairness. Information Systems Journal, 32(4), 754-818.
    • Caton, S., & Haas, C. (2020). Fairness in machine learning: A survey. ACM Computing Surveys.
    • Green, B. (2022). Escaping the impossibility of fairness: From formal to substantive algorithmic fairness. Philosophy & Technology, 35(4), 90.
    • Mitchell, S., Potash, E., Barocas, S., D'Amour, A., & Lum, K. (2021). Algorithmic fairness: Choices, assumptions, and definitions. Annual Review of Statistics and Its Application, 8, 141-163.
    • Pfeiffer, J., Gutschow, J., Haas, C., Möslein, F., Maspfuhl, O., Borgers, F., & Alpsancar, S. (2023). Algorithmic Fairness in AI: An Interdisciplinary View. Business & Information Systems Engineering, 65(2), 209-222.
    • Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019, January). Fairness and abstraction in sociotechnical systems. In Proceedings of the conference on fairness, accountability, and transparency (pp. 59-68).
    • Sonboli, N., Burke, R., Ekstrand, M., & Mehrotra, R. (2022). The multisided complexity of fairness in recommender systems. AI magazine, 43(2), 164-176.
    • Starke, C., Baleis, J., Keller, B., & Marcinkowski, F. (2022). Fairness perceptions of algorithmic decision-making: A systematic review of the empirical literature. Big Data & Society, 9(2), 20539517221115189.
    • Wang, X., Zhang, Y., & Zhu, R. (2022). A brief review on algorithmic fairness. Management System Engineering, 1(1), 7.
    • Wong, P. H. (2020). Democratizing algorithmic fairness. Philosophy & Technology, 33, 225-244.