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Publikationstyp: Beitrag in wissenschaftlicher Zeitschrift
Art der Begutachtung: Peer review (Publikation)
Titel: Assessing deep learning : a work program for the humanities in the age of artificial intelligence
Autor/-in: Segessenmann, Jan
Stadelmann, Thilo
Davison, Andrew
Dürr, Oliver
et. al: No
DOI: 10.1007/s43681-023-00408-z
10.21256/zhaw-29422
Erschienen in: AI and Ethics
Erscheinungsdatum: 21-Dez-2023
Verlag / Hrsg. Institution: Springer
ISSN: 2730-5953
2730-5961
Sprache: Englisch
Schlagwörter: Deep learning; Anthropology; Humanities; Artificial intelligence; Ethics; Philosophy
Fachgebiet (DDC): 006: Spezielle Computerverfahren
301: Soziologie und Anthropologie
Zusammenfassung: Following the success of deep learning (DL) in research, we are now witnessing the fast and widespread adoption of arti cial intelligence (AI) in daily life, influencing the way we act, think, and organize our lives. However, much still remains a mystery when it comes to how these systems achieve such high performance and why they reach the outputs they do. This presents us with an unusual combination: of technical mastery on the one hand, and a striking degree of mystery on the other. This conjunction is not only fascinating, but it also poses considerable risks, which urgently require our attention. Awareness of the need to analyze ethical implications, such as fairness, equality, and sustainability, is growing. However, other dimensions of inquiry receive less attention, including the subtle but pervasive ways in which our dealings with AI shape our way of living and thinking, transforming our culture and human self-understanding. If we want to deploy AI positively in the long term, a broader and more holistic assessment of the technology is vital, involving not only scienti c and technical perspectives but also those from the humanities. To this end, we present outlines of a work program for the humanities that aim to contribute to assessing and guiding the potential, opportunities, and risks of further developing and deploying DL systems. This paper contains a thematic introduction (section 1), an introduction to the workings of DL for non-technical readers (section 2), and a main part, containing the outlines of a work program for the humanities (section 3). Readers familiar with DL might want to ignore 2 and instead directly read 3 after 1.
URI: https://digitalcollection.zhaw.ch/handle/11475/29422
Volltext Version: Publizierte Version
Lizenz (gemäss Verlagsvertrag): CC BY 4.0: Namensnennung 4.0 International
Departement: School of Engineering
Organisationseinheit: Centre for Artificial Intelligence (CAI)
Publiziert im Rahmen des ZHAW-Projekts: Stability of self-organizing net fragments as inductive bias for next-generation deep learning
Enthalten in den Sammlungen:Publikationen School of Engineering

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Segessenmann, J., Stadelmann, T., Davison, A., & Dürr, O. (2023). Assessing deep learning : a work program for the humanities in the age of artificial intelligence. AI and Ethics. https://doi.org/10.1007/s43681-023-00408-z
Segessenmann, J. et al. (2023) ‘Assessing deep learning : a work program for the humanities in the age of artificial intelligence’, AI and Ethics [Preprint]. Available at: https://doi.org/10.1007/s43681-023-00408-z.
J. Segessenmann, T. Stadelmann, A. Davison, and O. Dürr, “Assessing deep learning : a work program for the humanities in the age of artificial intelligence,” AI and Ethics, Dec. 2023, doi: 10.1007/s43681-023-00408-z.
SEGESSENMANN, Jan, Thilo STADELMANN, Andrew DAVISON und Oliver DÜRR, 2023. Assessing deep learning : a work program for the humanities in the age of artificial intelligence. AI and Ethics. 21 Dezember 2023. DOI 10.1007/s43681-023-00408-z
Segessenmann, Jan, Thilo Stadelmann, Andrew Davison, and Oliver Dürr. 2023. “Assessing Deep Learning : A Work Program for the Humanities in the Age of Artificial Intelligence.” AI and Ethics, December. https://doi.org/10.1007/s43681-023-00408-z.
Segessenmann, Jan, et al. “Assessing Deep Learning : A Work Program for the Humanities in the Age of Artificial Intelligence.” AI and Ethics, Dec. 2023, https://doi.org/10.1007/s43681-023-00408-z.


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