Minor Politics, Major Consequences : Epistemic Challenges of Metadata and the Contribution of Image Recognition
: Epistemic Challenges of Metadata and the Contribution of Image Recognition
Sie können Bookmarks mittels Listen verwalten, loggen Sie sich dafür bitte in Ihr SLUB Benutzerkonto ein.
Medientyp:
E-Artikel
Titel:
Minor Politics, Major Consequences : Epistemic Challenges of Metadata and the Contribution of Image Recognition
:
Epistemic Challenges of Metadata and the Contribution of Image Recognition
Beschreibung:
<jats:title>Abstract</jats:title>
<jats:p> Metadata is part of our knowledge systems and, so, represents and perpetuates political hierarchies and perceptions of relevance. While some of these have come up for scrutiny in the discourses on digitization, some ‘minor’ issues have gone unnoticed and a few new mechanisms of imbalance have escaped attention as well. Yet, all of these, too, influence the usability of digital image collections. This paper traces three fields of ‘minor politics’ and their epistemic consequences, both in general and in particular, with respect to the study of architecture and its visual representation: first, the intrinsic logic of the original collections and their digital representation; second, the role of support staff in the course of digitization and data transfer; and, third, keywording as a matter of disciplinary habitus. It underlines the ‘political’ role of metadata within the context of knowledge production, even on the local level of a single database, and connects to the implementation of contemporary technologies like computer vision and artificial intelligence for image content classification and the creation of metadata. Given the abundance of digitally available (historical) images, image content recognition and the creation of metadata by artificial intelligence are sheer necessities in order to make millions of hitherto unexplored images available for research. At the same time, the challenge to overcome existing colonial and other biases in the training of AI remains. Hence, we are once again tasked to reflect on the delicate criterion of objectivity. The second part of this paper focuses on research done in the ArchiMediaL project (archimedial.eu); it demonstrates both the potentials and the risks of applying artificial intelligence for metadata creation by addressing the three fields mentioned above through the magnifying glass of programming.</jats:p>