Hybrid Intelligence for Digital Humanities
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911929433325568 |
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| author | de Boer, Victor Stork, Lise |
| author_facet | de Boer, Victor Stork, Lise |
| contents | In this paper, we explore the synergies between Digital Humanities (DH) as a discipline and Hybrid Intelligence (HI) as a research paradigm. In DH research, the use of digital methods and specifically that of Artificial Intelligence is subject to a set of requirements and constraints. We argue that these are well-supported by the capabilities and goals of HI. Our contribution includes the identification of five such DH requirements: Successful AI systems need to be able to 1) collaborate with the (human) scholar; 2) support data criticism; 3) support tool criticism; 4) be aware of and cater to various perspectives and 5) support distant and close reading. We take the CARE principles of Hybrid Intelligence (collaborative, adaptive, responsible and explainable) as theoretical framework and map these to the DH requirements. In this mapping, we include example research projects. We finally address how insights from DH can be applied to HI and discuss open challenges for the combination of the two disciplines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_15374 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hybrid Intelligence for Digital Humanities de Boer, Victor Stork, Lise Computers and Society Artificial Intelligence In this paper, we explore the synergies between Digital Humanities (DH) as a discipline and Hybrid Intelligence (HI) as a research paradigm. In DH research, the use of digital methods and specifically that of Artificial Intelligence is subject to a set of requirements and constraints. We argue that these are well-supported by the capabilities and goals of HI. Our contribution includes the identification of five such DH requirements: Successful AI systems need to be able to 1) collaborate with the (human) scholar; 2) support data criticism; 3) support tool criticism; 4) be aware of and cater to various perspectives and 5) support distant and close reading. We take the CARE principles of Hybrid Intelligence (collaborative, adaptive, responsible and explainable) as theoretical framework and map these to the DH requirements. In this mapping, we include example research projects. We finally address how insights from DH can be applied to HI and discuss open challenges for the combination of the two disciplines. |
| title | Hybrid Intelligence for Digital Humanities |
| topic | Computers and Society Artificial Intelligence |
| url | https://arxiv.org/abs/2406.15374 |