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Challenges of data-driven technologies for social inequality and privacy: empirical research on context and public perceptions
Gerdon, Frederic
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URN:
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urn:nbn:de:bsz:180-madoc-674360
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Dokumenttyp:
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Dissertation
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Erscheinungsjahr:
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2024
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Ort der Veröffentlichung:
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Mannheim
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Hochschule:
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Universität Mannheim
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Gutachter:
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Gummer, Tobias
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Datum der mündl. Prüfung:
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24 April 2024
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Sprache der Veröffentlichung:
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Englisch
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Einrichtung:
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Außerfakultäre Einrichtungen > GESS - CDSS (SOWI)
Fakultät für Sozialwissenschaften > Social Data Science and Methodology (Keusch 2022-)
Außerfakultäre Einrichtungen > MZES - Arbeitsbereich A
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Fachgebiet:
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300 Sozialwissenschaften, Soziologie, Anthropologie
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Freie Schlagwörter (Englisch):
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digitalization , artifical intelligence , data-driven technologies , algorithmic decision-making , privacy , survey experiments , public opinion
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Abstract:
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Data-driven technologies are increasingly used by private and public entities for various purposes, promising efficiency gains and the delivery of new services and products. However, societal impacts of these technologies with respect to, among others, social inequality and privacy raise questions of the legitimate use of these technologies. In this dissertation, I emphasize the importance contextual norms to judge the legitimacy of using these technologies (following the notion of “contextual integrity” by Helen Nissenbaum), and highlight context-specific public opinion as a relevant factor for ethical evaluations of data-driven technologies. As an overview, first, processes of algorithmic decision-making as a major data-driven technology are described to show how exactly they may impact social inequality. Then, empirically, results from three survey experiments are presented which demonstrate that public opinion on fairness and privacy issues relating to data-driven technologies depend on social context and timing. Concluding this dissertation, I point out that we need to measure public opinion context-specifically and consider it as one among multiple elements of ethical evaluations of data-driven technologies.
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 | Dieser Eintrag ist Teil der Universitätsbibliographie. |
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