Hybrid Intelligence for Digital Humanities
This work addresses the problem of adapting AI systems to meet the specific needs of Digital Humanities researchers, but it is incremental as it primarily maps existing concepts without introducing new methods or data.
The paper tackles the integration of Hybrid Intelligence (HI) into Digital Humanities (DH) by identifying five DH requirements for AI systems, such as collaboration with scholars and supporting data criticism, and maps them to the CARE principles of HI, using example projects to illustrate the synergies.
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.