Assessing deep learning: a work program for the humanities in the age of artificial intelligence
-
Published:2023-12-21
Issue:
Volume:
Page:
-
ISSN:2730-5953
-
Container-title:AI and Ethics
-
language:en
-
Short-container-title:AI Ethics
Author:
Segessenmann JanORCID, Stadelmann ThiloORCID, Davison AndrewORCID, Dürr OliverORCID
Abstract
AbstractFollowing the success of deep learning (DL) in research, we are now witnessing the fast and widespread adoption of artificial 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 scientific and technical perspectives, but also those from the humanities. To this end, we present outlines of awork programfor 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 (Sect. 1), an introduction to the workings of DL for non-technical readers (Sect. 2), and a main part, containing the outlines of a work program for the humanities (Sect. 3). Readers familiar with DL might want to ignore 2 and instead directly read 3 after 1.
Funder
University of Fribourg
Publisher
Springer Science and Business Media LLC
Subject
General Earth and Planetary Sciences
Reference405 articles.
1. Hinton, G.E., Osindero, S., Teh, Y.-W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006). https://doi.org/10.1162/neco.2006.18.7.1527 2. Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H.: Greedy layer-wise training of deep networks. In: Schölkopf, B., Platt, J., Hoffman, T. (eds.) Advances in Neural Information Processing Systems, vol. 19. MIT Press (2006). https://proceedings.neurips.cc/paper_files/paper/2006/file/5da713a690c067105aeb2fae32403405-Paper.pdf 3. Ranzato, M.a., Poultney, C., Chopra, S., Cun, Y.: Efficient learning of sparse representations with an energy-based model. In: Schölkopf, B., Platt, J., Hoffman, T. (eds.) Advances in Neural Information Processing Systems, vol. 19. MIT Press (2006). https://proceedings.neurips.cc/paper_files/paper/2006/file/87f4d79e36d68c3031ccf6c55e9bbd39-Paper.pdf 4. Stadelmann, T., Amirian, M., Arabaci, I., Arnold, M., Duivesteijn, G.F., Elezi, I., Geiger, M., Lörwald, S., Meier, B.B., Rombach, K., et al.: Deep Learning in the wild. In: IAPR Workshop on Artificial Neural Networks in Pattern Recognition, pp. 17–38 (2018). Springer 5. Yan, P., Abdulkadir, A., Rosenthal, M., Schatte, G.A., Grewe, B.F., Stadelmann, T.: A comprehensive survey of deep transfer learning for anomaly detection in industrial time series: methods, applications, and directions. Preprint (2023). https://doi.org/10.48550/arXiv.2307.05638
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
|
|