Thema5053
Abschlussarbeitstyp: Bachelor, Master
Betreuer: Gabriela Ciolacu
Forschungsgruppe: Critical Information Infrastructures
Archivierungsnummer: 5053
Abschlussarbeitsstatus: Offen
Beginn:
01. Januar 2024
Abgabe: unbekannt
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 algo-rithmic 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, stakehold-ers. It also frequently requires integrating a multifaceted perspective of information systems (IS), con-sidering the conflicts between the parties understanding of it and accounting for legal, social and tech-nical limitations. The design, development, and testing of fairness in diverse IS contexts are being nega-tively impacted by the lack of agreement on what constitutes fairness due to the existent tensions be-tween 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 complicat-ed 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. Objective(s): • 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 da-ta/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, eco-nomical, technical or purely theoretical angle. The thesis allows you to gain a broad knowledge in fair-ness and to make a significant contribution towards a scientifically sound fundament.
Method(s): • Since the suggested topic is broad, a variety of techniques could be used, beginning with a re-view of the literature and moving on to qualitative methods such as interviews, or quantitative methods like algorithmic interventions, experiment design, or mathematical proofs. Literature: • Dolata, M., Feuerriegel, S., & Schwabe, G. (2022). A sociotechnical view of algorithmic fair-ness. Information Systems Journal, 32(4), 754-818. • Caton, S., & Haas, C. (2020). Fairness in machine learning: A survey. ACM Computing Sur-veys. • 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 fair-ness 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.