The AI:CULT project addresses the gap between AI and our digital cultural heritage. Cultural heritage data is rarely objective data. The very reasons for certain heritage data to be preserved, its interpretation throughout time, and the way heritage data is accessed after digitalisation is all subject to strong biases. The inherent richness, subjectivity and polyvocal nature of cultural heritage data limits and often even rules out the responsible use of AI. How do we model that “Seventeenth Century” and “The Golden Age” refer to the same era, yet are not fully synonymous and carry different semantic payloads? 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.
The project addresses two case studies with societal parties tasked with providing access to national heritage, and who have voiced their vested interest in using AI for their workflows: the National Library (KB) and the Institute for Sound and Vision (NISV): (i) automatically analysing and enriching object-level descriptions and (ii) creating data stories and narratives from raw collection data. Both institutions acknowledge that the straightforward application of AI reflects biases present in the training data. In the AI:CULT project bias detection and filtering methods will be developed that will be directly tested on the heritage institutions’ workfloors.
The two Ph.D. students on this project will be based at KNAW Humanities Cluster and CWI in Amsterdam.
Source: Cultural AI - AI:CULT