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    Towards Transnational Fairness in Machine Learning: A Case Study in Disaster Response Systems
    with Cem Kozcuer and Felix Bießmann
    Minds and Machines 34 (2): 1-26. 2024.
    Research on fairness in machine learning (ML) has been largely focusing on individual and group fairness. With the adoption of ML-based technologies as assistive technology in complex societal transformations or crisis situations on a global scale these existing definitions fail to account for algorithmic fairness transnationally. We propose to complement existing perspectives on algorithmic fairness with a notion of transnational algorithmic fairness and take first steps towards an analytical f…Read more