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https://doi.org/10.21256/zhaw-29422Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Segessenmann, Jan | - |
| dc.contributor.author | Stadelmann, Thilo | - |
| dc.contributor.author | Davison, Andrew | - |
| dc.contributor.author | Dürr, Oliver | - |
| dc.date.accessioned | 2023-12-22T13:19:32Z | - |
| dc.date.available | 2023-12-22T13:19:32Z | - |
| dc.date.issued | 2023-12-21 | - |
| dc.identifier.issn | 2730-5953 | de_CH |
| dc.identifier.issn | 2730-5961 | de_CH |
| dc.identifier.uri | https://digitalcollection.zhaw.ch/handle/11475/29422 | - |
| dc.description.abstract | 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. | de_CH |
| dc.language.iso | en | de_CH |
| dc.publisher | Springer | de_CH |
| dc.relation.ispartof | AI and Ethics | de_CH |
| dc.rights | http://creativecommons.org/licenses/by/4.0/ | de_CH |
| dc.subject | Deep learning | de_CH |
| dc.subject | Anthropology | de_CH |
| dc.subject | Humanities | de_CH |
| dc.subject | Artificial intelligence | de_CH |
| dc.subject | Ethics | de_CH |
| dc.subject | Philosophy | de_CH |
| dc.subject.ddc | 006: Spezielle Computerverfahren | de_CH |
| dc.subject.ddc | 301: Soziologie und Anthropologie | de_CH |
| dc.title | Assessing deep learning : a work program for the humanities in the age of artificial intelligence | de_CH |
| dc.type | Beitrag in wissenschaftlicher Zeitschrift | de_CH |
| dcterms.type | Text | de_CH |
| zhaw.departement | School of Engineering | de_CH |
| zhaw.organisationalunit | Centre for Artificial Intelligence (CAI) | de_CH |
| dc.identifier.doi | 10.1007/s43681-023-00408-z | de_CH |
| dc.identifier.doi | 10.21256/zhaw-29422 | - |
| zhaw.funding.eu | No | de_CH |
| zhaw.originated.zhaw | Yes | de_CH |
| zhaw.publication.status | publishedVersion | de_CH |
| zhaw.publication.review | Peer review (Publikation) | de_CH |
| zhaw.webfeed | Datalab | de_CH |
| zhaw.webfeed | DIZH Fellowship | de_CH |
| zhaw.webfeed | Machine Perception and Cognition | de_CH |
| zhaw.webfeed | ZHAW digital | de_CH |
| zhaw.funding.zhaw | Stability of self-organizing net fragments as inductive bias for next-generation deep learning | de_CH |
| zhaw.author.additional | No | de_CH |
| zhaw.display.portrait | Yes | de_CH |
| Appears in collections: | Publikationen School of Engineering | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2023_Segessenmann-etal_Assessing-deep-learning-humanities.pdf | 1.93 MB | Adobe PDF |  View/Open |
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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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