AI in libraries and archives: the dual role of heritage institutions
Public libraries and archives find themselves in a unique and intriguing position within the landscape of artificial intelligence. On the one hand, they curate the treasures of printed and written culture that serve as invaluable resources for training and feeding modern language models. On the other hand, these institutions have themselves become intensive users of that very same technology. They face the challenge of making massive, often unstructured collections accessible to a broad public without losing sight of their core societal values.
The debate surrounding digital innovation in the heritage sector usually centers on efficiency or cost savings. Yet the real value of this technology lies elsewhere: in transforming the way people access historical and cultural information. This requires a careful balance of opportunities, with an eye on the long term and the specific duties inherent to a public institution. Anyone who understands the underlying mechanisms behind these systems quickly realizes that the choices being made today will have far-reaching implications for the future of information access.
The unique position: source and user all at once
Heritage institutions are traditionally the guardians of reliable information. Their shelves and servers are packed with material collected, organized, and cataloged over generations. This makes them a primary source for AI developers searching for high-quality, written Dutch text. After all, models require vast amounts of quality material to accurately understand and generate language. This immediately creates tension: the data these institutions manage is used to train commercial and open-source systems, while the institutions themselves sometimes struggle to keep their digital infrastructure up to standard.
At the same time, libraries and archives are active users of the technology. They deploy modern models to streamline their own workflows, from cataloging incoming acquisitions to making centuries-old correspondence searchable. This dual role means they must not only consider how they interact with third-party technology, but also evaluate the intrinsic value of their own collections. Just as in other fields, as detailed in the background on AI and the creative sector, the question of who benefits from the digital processing of culture is a fundamental issue that calls for policy clarity.
Applications that matter: unlocking hidden treasures
Much of what is stored in archives and libraries is practically inaccessible to the average seeker. The material was never cataloged at a detailed level, or it consists of handwritten documents from earlier centuries that are illegible to the modern eye. Traditional indexing methods take too much time and manpower to process these mountains of paper manually. This is where advanced learning models prove their worth, although the specific implications of these types of processes are often underestimated.
Consider, for instance, automated handwriting recognition in early printed books and archival records. Where manual transcription by paleographers takes years, trained systems can quickly convert large volumes of historical texts into searchable digital formats. Automated generation of keywords and summaries also helps enrich collections. These types of processes closely align with what is technically discussed in guides on RAG for beginners, where external documents are linked to language models to generate targeted answers based on specific sources.
In addition, the nature of searching is fundamentally changing with the rise of semantic search. Traditional search engines rely on exact keywords: if the word does not appear in the description, you will not find the document. Semantic search examines the meaning and context of the query. A user can ask a question in natural language and retrieve documents that cover that topic, even if entirely different terms are used in the text. This paves the way for a more intuitive approach to research, which closely mirrors the methods also applied in searching documents locally within professional environments.
Searching by meaning versus web search engines: the requirement of completeness
It is tempting to compare an archival search function with a standard internet search engine, but the underlying goals differ fundamentally. Anyone searching the web for a recipe or a travel tip is satisfied with the best answer the search engine can find. An archive user or historian has an entirely different motivation: they often seek comprehensive information and require verifiable certainty about what is and is not stored.
If a web search engine misses a relevant result, the user usually does not notice. In an archival setting, however, the omission of a single crucial letter or deed can render historical research incomplete or inaccurate. This means semantic search systems in an archive cannot afford to guess or creatively fill in what might be there. The reliability of the underlying index must be absolute. The model may help point the way, but the structure must remain strictly factual and verifiable.
Findability versus reliability: the necessity of source attribution
A well-known phenomenon in generative language models is 'hallucination': where a model invents facts, names, or connections with high confidence that do not exist in the source material. While this is frustrating in a commercial chatbot, it is unacceptable in a cultural heritage context. When a visitor asks questions about local history or a specific collection via a smart assistant, the system must not fabricate answers.
For this reason, direct and verifiable referencing to the actual archival object is not an optional extra, but a strict prerequisite for any responsible implementation. Every claim a model makes about the collection must be directly tied to a specific inventory number, scan, or page. This requires architectures where the language model is strictly constrained and permitted to draw exclusively from verified, pre-selected documents. Those who want to delve deeper into how students and researchers navigate the reliability of these types of tools can also consult overviews for AI tools for research and study, where the focus similarly lies on verifiability.
The public mission: neutrality, equal access, and education
Public libraries and archives serve a distinct public mission. They exist for everyone, regardless of background, income, or digital literacy. The rise of artificial intelligence touches the core of this mandate in several ways.
First is the issue of equal access. If advanced search systems and digital services become available exclusively through paid platforms or complex interfaces, a new digital divide threatens to emerge. Libraries have traditionally bridged this divide by providing free access to information and technology. This means they also have a role to play in explaining how AI works to visitors—not by buying into the hype, but by objectively assessing what the technology can and cannot do, highlighting the risks of bias, and teaching how to critically evaluate generated answers.
Secondly, neutrality is a core asset. Heritage institutions must not project political or commercial preferences in their collection presentations. When deploying commercial models to make information accessible or to present it, they must ensure these systems do not provide a distorted view or systematically disadvantage certain perspectives. Safeguarding that editorial and algorithmic independence is a governance responsibility of the highest order.
Rights and provenance: a governance issue
Not every document held in a library or archive is freely usable. Archival items may contain privacy-sensitive personal data, and printed works may be subject to copyright. Training models on heritage data or making it accessible through public interfaces introduces complex legal questions.
It is a misconception to think this is a purely technical problem that can be solved with the right software. Above all, it is a governance consideration. Institutions must determine on a collection-by-collection basis which material is suitable for digital processing and what restrictions apply. Anyone wishing to learn more about the broader legal frameworks surrounding the use of protected works can consult the analyses on AI and copyright which elaborate further on the rules regarding data use and intellectual property.
Historical terminology and the pitfall of outdated language
Descriptions from the past are shaped by the era in which they were written. Archives contain inventories and catalog entries from the nineteenth and twentieth centuries that feature terminology now perceived as offensive, stigmatizing, or simply outdated. Human archivists understand this historical context and can explain why certain words were used at the time.
When an unfiltered language model is deployed on these historical descriptions, it lacks that nuanced historical sensitivity. The model may replicate outdated or offensive terms in contemporary responses without the necessary context, or it might amplify the terminology rather than nuance it. This calls for deliberate choices in how training data is curated and how prompts are structured, ensuring that the past remains accessible for historical research without being presented unfiltered to the public as active truth.
Sustainability of choices: thinking in decades
Heritage institutions naturally think in decades or even centuries; after all, their collections must be preserved for generations. This stands in stark contrast to developers of artificial intelligence and commercial cloud services, who often think in years or even months. Models are constantly updated, API structures are modified, and popular services can abruptly disappear or change ownership.
When a library or archive becomes overly dependent on a specific, external commercial service for its digital accessibility, it risks digital impoverishment as soon as that service ceases to exist or changes its terms. The sustainability of choices is therefore a crucial theme. This is why many institutions prefer open standards, open-source models that can be run locally or on their own controlled servers, and standardized metadata formats. Only in this way does the digital infrastructure remain manageable and independent over the long term.
Practical steps for small institutions
Not every library or regional archive has a large IT budget or a data analysis department. For smaller institutions, the sheer volume of offerings and the speed of developments can be overwhelming. Yet one does not need to invest millions right away to make progress.
Experience shows that a pragmatic, small-scale approach is the most sustainable. It starts with identifying one's own digital bottlenecks: what does staff spend the most time on, and which part of the collection remains structurally unfindable? Next, organizations can look into accessible, open-source tools that can be deployed securely. By opting for proven standards and collaborating within broader networks, smaller organizations can also reap the benefits of modern technology without losing their core identity or independence.
| Comparison | Traditional approach | AI-assisted approach |
|---|---|---|
| Making manuscripts accessible | Manual transcription by specialists (very slow) | Automated text recognition with post-verification |
| Search behavior | Exact keywords and fixed categories | Semantic search based on meaning and context |
| Sustainability | Long-term, physical and stable digital | Rapid model cycles, requiring open standards |
