Summer School: Machine Learning for Digital Scholarly Editions - Keynote 1

Clemens Neudecker studied Philosophy, Computer Science and Political Science at LMU Munich and works as Head of Data Science in the Information and Data Management Department of the Staatsbibliothek zu Berlin - Preußischer Kulturbesitz. The main focus of his work and research lies in Computer Vision, Natural Language Processing and Machine Learning/Artificial Intelligence and their applications in the context of digitization and the Digital Humanities.

"Context matters. Opportunities and challenges when working with artificial intelligence and cultural heritage data" The advances made in the field of machine learning/artificial intelligence (ML) offer a range of opportunities for libraries and digital scholarship. In projects such as Mensch.Maschine.Kultur, the Staatsbibliothek zu Berlin - Preußischer Kulturbesitz (SBB) is developing ML technologies for a wide range of applications: from text and layout recognition and image analysis to information extraction, machine-assisted subject indexing and, last but not least, the provision of collections as data and their digital curation. On the other hand, the historical and cultural contexts must always be taken into account when using ML technologies in combination with historical sources and cultural heritage materials. Collections digitized by libraries are heterogeneous in terms of the period covered, the perspectives, places or regions they contain and the cultural contexts in which they must be placed. Historical documents often contain distortions that no longer correspond to today's ethical values. While historians are trained to classify sources and apply source criticism as a methodological tool, AI systems developed by industry are primarily trained on modern texts from the Internet and cannot do this. Using the example of SBB's experience with machine learning and AI, this talk aims to provide insights into practical applications while at the same time raising awareness for a conscious and responsible approach to ML and cultural heritage data.
Online:  https://unimeet.uni-graz.at/b/sch-pwq-3w3-ioy