The Infinite Archive: AI in Libraries and Information Science

Libraries are not merely warehouses, and an archive is not preserved because a model can summarize it. Libraries select, describe, provide access, protect privacy, negotiate rights, teach research practice, and maintain cultural memory across changing technologies. AI can assist discovery, transcription, metadata, and service operations, but it can also invent citations, flatten contested descriptions, expose reader behavior, and create new vendor dependencies.

The responsible 2026 objective is not an “infinite archive.” It is durable, inspectable access to finite collections under clear rights and stewardship. AI should make professional judgment more scalable while preserving provenance, intellectual freedom, accessibility, and the user’s ability to see where information came from.

1. Start with a service problem

Define whether the institution needs better known-item search, exploratory discovery, OCR correction, transcription, subject suggestion, duplicate detection, reference triage, collection assessment, accessibility, or preservation planning. These tasks have different evidence, risks, and success measures. A general chatbot is rarely a sufficient requirements document.

Name the collection, audience, languages, rights status, expected query, acceptable error, staff owner, and fallback. Compare AI with better metadata, interface changes, authority control, digitization, staff training, or a federated search improvement. The least complex intervention that improves access is often the most sustainable.

2. Preserve provenance in discovery

A conversational answer can hide the difference between catalogue metadata, licensed full text, a finding aid, a generated summary, and outside web content. The interface should identify which collections were searched, the query or filters, the matching records, and the source for each claim. Stable links and identifiers must remain available beneath the prose.

Retrieval should respect field semantics, editions, dates, creators, subjects, holdings, and collection boundaries. Avoid presenting an answer when the system has weak evidence. Let users inspect snippets in context, refine the search, switch to the native catalogue, and report a problem. A fluent response is not a bibliographic record.

3. Treat metadata suggestions as proposals

Machine learning can suggest classifications, names, subjects, language, summaries, or entity links. It can also reproduce outdated terminology, confuse people with similar names, assign a dominant-language category to a local concept, or infer sensitive attributes. Keep the original record and identify machine-generated fields and model versions.

Use controlled vocabularies and authority files where appropriate, but preserve local and community knowledge rather than forcing every collection into one ontology. Require review for access points that affect discovery, rights, identity, or cultural sensitivity. Track acceptance, edits, rejection reasons, and subgroup performance by language, format, period, and collection.

4. Improve OCR without rewriting history

Optical character recognition and handwriting recognition can make scanned newspapers, books, and manuscripts searchable. Errors remain common with damaged paper, unusual type, complex layouts, tables, marginalia, historic spelling, right-to-left scripts, mixed languages, and handwriting. Generative correction can silently replace an unfamiliar word with a plausible modern one.

Keep the source image, raw OCR, corrected text, confidence, layout coordinates, software version, and correction history. Let researchers distinguish diplomatic transcription from normalized reading text. Sample quality by title, year, language, print condition, and page type. For quotations or names, direct users to the image rather than presenting generated text as the artifact.

5. Separate access generation from preservation

AI-derived captions, transcripts, embeddings, summaries, or enhanced images can improve access, but they are derivatives. Preservation protects the authentic digital object, its relationships, events, rights, and technical context over time. A generated surrogate cannot replace a master file or physical original.

The Library of Congress maintains PREMIS preservation metadata, an international standard supporting long-term usability of digital objects. Its model of objects, events, agents, and rights provides a disciplined way to record transformations. Store fixity, format, provenance, validation, migrations, and dependencies. Treat model inference as a documented event, not an invisible edit.

6. Govern copyright and licensing by collection

The right to digitize, index, make an accessibility copy, provide a snippet, train a model, or send content to a vendor differs by jurisdiction, license, material, purpose, and user. Publicly accessible does not mean free of copyright, privacy, contract, donor, Indigenous knowledge, or ethical restrictions.

IFLA’s April 2025 statement on copyright and artificial intelligence addresses the issue from the perspective of libraries and users, but it is a policy statement, not binding law. Conduct collection-level rights analysis with qualified local expertise. Record the legal or contractual basis, permitted processing, output limits, retention, and takedown process. Do not upload licensed or restricted content to a general model without authorization.

7. Protect reader privacy and intellectual freedom

Search queries, borrowing, reading duration, reference questions, annotations, and accessibility needs can reveal religion, politics, health, immigration, sexuality, legal problems, and research strategy. Personalization requires data, but a library’s mission may favor confidentiality and unprofiled access over marginal ranking gains.

Minimize collection and retention, separate identity from search logs, restrict staff and vendor access, and provide a non-personalized mode. Do not use patron histories to train general models by default. Assess subpoenas, security incidents, cross-border processing, children, and shared devices. Explain what the assistant records before a user asks a sensitive question.

8. Design for inclusion and multilingual access

AI may create captions, plain-language summaries, translation, text-to-speech, or alternative search routes. These can expand access but may erase nuance, mishandle names, or underperform for low-resource languages and disability contexts. Test with the communities the service intends to support.

Keep original-language metadata and content alongside translations. Mark generated translations, expose confidence or review status, and support correction. Evaluate right-to-left layout, screen readers, keyboard navigation, low bandwidth, and non-chat interfaces. Personalization must not narrow discovery into a filter bubble or prevent users from browsing beyond predicted interests.

9. Make reference assistants evidence-bound

A library assistant can explain catalogue use, suggest search terms, surface databases, or draft a research strategy. It should not fabricate holdings, citations, access conditions, or professional legal and medical advice. Ground answers in approved sources, expose citations, and limit the assistant to collections it can actually query.

Define when a librarian takes over: complex reference interviews, rare materials, sensitive topics, inaccessible resources, uncertainty, and reported errors. Preserve enough interaction context for service continuity without retaining more patron data than necessary. Measure successful source access and corrected misunderstandings, not only answer speed.

10. Procure for stewardship, not lock-in

Evaluate data use, model training, subprocessors, hosting region, security, accessibility, model updates, export, audit, incident response, deletion, intellectual property, environmental cost, and contract termination. Require stable identifiers and machine-readable export for records, annotations, embeddings where useful, and logs needed for institutional accountability.

OCLC’s Responsible Operations research agenda identifies technical, organizational, and social work across description, shared data, machine-actionable collections, workforce, services, and collaboration. The lesson is broader than one vendor: operations, skills, governance, and sustainability are part of the system. A pilot without a preservation and exit plan creates future backlog.

11. Validate with collection and community evidence

Create test sets across languages, periods, formats, subjects, rights conditions, known biases, damaged items, and difficult queries. Include known-item, exploratory, adversarial, ambiguous, and no-answer cases. Review with cataloguers, archivists, librarians, preservation specialists, accessibility experts, and relevant communities.

Measure retrieval quality, citation validity, OCR character and word error, metadata acceptance, correction time, accessibility, privacy incidents, staff workload, energy and cost, and user success. Report errors by collection rather than one average. Run a shadow mode, then a bounded pilot with stop criteria and a visible feedback path. Re-test after every model, index, prompt, or source change.

12. A practical stewardship gate

Deploy when the service objective is clear; source provenance remains visible; authoritative records and masters are preserved; rights and privacy are mapped; outputs are accessible and reviewable; vendors are governable and replaceable; and quality is measured across the real collection. If a model cannot cite, abstain, export, or be turned off, it should not become the sole discovery path.

For adjacent patterns, see AI in libraries and information services, RAG and knowledge quality, and knowledge-graph reasoning. The future library is dynamic because people can trace, challenge, reinterpret, and preserve knowledge—not because an algorithm claims to understand everything.

Source notes

Sources reviewed on 2026-07-30:

• IFLA Statement on Libraries and Artificial Intelligence — 2020 international library-policy statement covering intellectual freedom, equity, privacy, literacy, and ethical procurement.
• IFLA Statement on Copyright and Artificial Intelligence — policy statement approved by IFLA’s Governing Board and published 29 April 2025; not binding law.
• Library of Congress: PREMIS Preservation Metadata Maintenance Activity — current maintenance page for the international preservation-metadata standard; PREMIS 3.0 is listed as current.
• OCLC Research: Responsible Operations—Data Science, Machine Learning, and AI in Libraries — community-informed research agenda for operational, technical, organizational, and social governance.