Don't Erase, Inform! Detecting and Contextualizing Harmful Language in Cultural Heritage Collections
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866916769019461632 |
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| author | Mastromichalakis, Orfeas Menis Liartis, Jason Rose, Kristina Isaac, Antoine Stamou, Giorgos |
| author_facet | Mastromichalakis, Orfeas Menis Liartis, Jason Rose, Kristina Isaac, Antoine Stamou, Giorgos |
| contents | Cultural Heritage (CH) data hold invaluable knowledge, reflecting the history, traditions, and identities of societies, and shaping our understanding of the past and present. However, many CH collections contain outdated or offensive descriptions that reflect historical biases. CH Institutions (CHIs) face significant challenges in curating these data due to the vast scale and complexity of the task. To address this, we develop an AI-powered tool that detects offensive terms in CH metadata and provides contextual insights into their historical background and contemporary perception. We leverage a multilingual vocabulary co-created with marginalized communities, researchers, and CH professionals, along with traditional NLP techniques and Large Language Models (LLMs). Available as a standalone web app and integrated with major CH platforms, the tool has processed over 7.9 million records, contextualizing the contentious terms detected in their metadata. Rather than erasing these terms, our approach seeks to inform, making biases visible and providing actionable insights for creating more inclusive and accessible CH collections. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_24538 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Don't Erase, Inform! Detecting and Contextualizing Harmful Language in Cultural Heritage Collections Mastromichalakis, Orfeas Menis Liartis, Jason Rose, Kristina Isaac, Antoine Stamou, Giorgos Computation and Language Cultural Heritage (CH) data hold invaluable knowledge, reflecting the history, traditions, and identities of societies, and shaping our understanding of the past and present. However, many CH collections contain outdated or offensive descriptions that reflect historical biases. CH Institutions (CHIs) face significant challenges in curating these data due to the vast scale and complexity of the task. To address this, we develop an AI-powered tool that detects offensive terms in CH metadata and provides contextual insights into their historical background and contemporary perception. We leverage a multilingual vocabulary co-created with marginalized communities, researchers, and CH professionals, along with traditional NLP techniques and Large Language Models (LLMs). Available as a standalone web app and integrated with major CH platforms, the tool has processed over 7.9 million records, contextualizing the contentious terms detected in their metadata. Rather than erasing these terms, our approach seeks to inform, making biases visible and providing actionable insights for creating more inclusive and accessible CH collections. |
| title | Don't Erase, Inform! Detecting and Contextualizing Harmful Language in Cultural Heritage Collections |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2505.24538 |