AI, Cultural Heritage, and Bias: Some Key Queries That Arise from the Use of GenAI

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Abstract
Our article AI, cultural heritage, and bias examines the challenges and potential solutions for using machine learning to interpret and classify human memory and cultural heritage artifacts. We argue that bias is inherent in cultural heritage collections (CHCs) and their digital versions and that AI pipelines may amplify this bias. We hypothesise that effective AI methods require vast, well-annotated datasets with structured metadata, which CHCs often lack due to diverse digitisation practices and limited interconnectivity. This paper discusses the definition of bias in CHCs and other datasets, exploring how it stems from training data and insufficient humanities expertise in generative platforms. We conclude that scholarship, guidelines, and policies on AI and CHCs should address bias as both inherent and augmented by AI technologies. We recommend implementing bias mitigation techniques throughout the process, from collection to curation, to support meaningful curation, embrace diversity, and cater to future heritage audiences.
Funding Information
  • Wallenberg Autonomous Systems in Humanities and Society Programme (WASP-HS) (MAW 2020.0054)
  • project Quantifying Culture: A Study of AI and Cultural Heritage Collections (MAW 2020.0054)

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