The KB has been successful in receiving the awards for two research projects around AI technology. Both projects aim to develop new techniques based on Artificial Intelligence (AI) and also customized to the needs and the available data of heritage institutions. It is expected that project results will bring new insights and breakthrough in how to perceive biases in our heritage data.
Both projects will be embedded in the newly established research lab Cultural AI Lab, an initiative from KNAW Humanities Cluster, CWI, TNO, Rijksmuseum Amsterdam, Netherlands Institute Sound and Vision, and the KB.
Summaries of the projects:
Partners: KNAW Humanities Cluster, Museum voor Wereldculturen, Sound and Vision, Koninklijke Bibliotheek (KB)
Funder: Netwerk Digitaal Erfgoed
The SABIO project aims to get more insights in how to deal with bias in cultural heritage data. AI technology will be used to improve the metadata in the collection together with the knowledge of professionals to improve the technology. This approach will strengthen the interaction between scientific research and heritage domain. Customized search engines based on collection requirements present better and balanced context of heritage data and may disclose the alternative voices, opinions and narratives on our heritage. The project results will be made available for the entire NDE consortium members.
Project: Culturally Aware AI (AI:CULT)
Partners: KNAW Meertens Institute, CWI, KNAW Humanities Cluster, Sound and Vision, Koninklijke Bibliotheek (KB)
The AI:CULT project addresses the gap between AI and our digital cultural heritage. Cultural heritage data is biased and subjective. Current state of the art AI cannot deal with these subtleties in a way that does justice to the important role of the heritage institute as a trusted source of information. Thus, the heritage sector is under threat to be left out of the current global success of AI. AI:CULT will allow heritage institutes to use AI in ways that align with their role in society: transparent, inclusive, and keeping the user in control. AI:CULT will offer heritage institutes methods for the detection and filtering of bias in automatically generated classifications and descriptions of collection data in two case studies. In the AI:CULT project bias detection and filtering methods will be developed that will be directly tested on the heritage institutions’ workfloors and will be made available to all 70 partners in the Digital Heritage Network.