Diversifying Archival Collections with AI: Opportunities, Risks, and Practical Steps
Archives have never been neutral. Whose papers were saved, whose names were indexed, and whose stories were described in detail all reflect the priorities of the people who built the collection. This guide shows how cultural-heritage nonprofits, libraries, and community archives can use AI to surface underrepresented voices, speed up appraisal and description, and invite communities into collecting, while taking seriously the risks of bias, provenance, and consent that AI introduces along the way.
Every archive carries silences. For generations, the records that survived and were carefully cataloged tended to come from institutions and individuals who already held power, while the lives of working people, immigrant communities, Indigenous nations, and marginalized groups were often left undescribed, mislabeled, or never collected at all. The result is a historical record that looks more uniform than the society it claims to represent. Cultural-heritage nonprofits and community archives have spent decades trying to correct this, but the work is slow, and the backlogs of undescribed material in most institutions are enormous.
Artificial intelligence has arrived in this space with a genuinely useful promise. Tools that can transcribe handwriting, generate descriptions for images, recognize names and places in unstructured text, and create searchable metadata at scale could help institutions finally process the hidden corners of their holdings. At its best, AI can surface names that were never indexed, connections that were never documented, and records that were never findable simply because no one had the time to describe them. For a small archive with one part-time archivist and a storeroom of unprocessed boxes, that promise is hard to ignore.
Yet the same technology that can widen access can also deepen the very exclusions it claims to fix. AI does not correct for bias on its own. It learns from data that already reflects historical prejudice, and it can replicate and amplify those patterns at a speed and scale no human cataloger ever could. A model that struggles with non-English names, that misreads certain handwriting styles, or that describes images through a narrow cultural lens will quietly push marginalized records further into the margins. Provenance, attribution, and the consent of the communities whose lives are documented add further weight to the ethical stakes.
This article is written for leaders and staff at cultural-heritage nonprofits, libraries, museums, and community archives who want to use AI thoughtfully. We look at where the opportunities are real, where the risks are sharp, and how to move forward with practical steps that keep human judgment at the center. For organizations still shaping their broader approach, it pairs well with our guidance on building a strategic plan for AI and the foundational steps in our nonprofit leaders guide to AI.
Why Archival Diversity Is a Problem Worth Solving
Before reaching for any tool, it helps to be precise about what archival diversity actually means and why the gaps exist. Collections become skewed at several points. Appraisal decisions, the choices about what to accept and keep, have historically favored the records of dominant institutions. Description practices have used language and categories that erase or distort the experiences of marginalized people. And processing priorities mean that the collections most likely to document underrepresented communities are often the ones left unprocessed at the back of the queue, invisible to anyone searching the catalog.
The consequence is not abstract. A researcher trying to trace a family history, a community group seeking evidence of its own past, or a journalist investigating an overlooked story all depend on what is findable. If a collection is technically held but never described, it might as well not exist for the person searching. Diversity in archives is therefore as much about discoverability and description as it is about acquisition. Material that sits in boxes without metadata cannot tell its story, and the people most affected by those silences are usually the ones already underrepresented.
This framing matters for how we evaluate AI. The most valuable contribution AI can make is not flashy generation but the patient, large-scale work of making hidden material findable: transcribing, indexing, tagging, and connecting. That is precisely the work that has been impossible to do by hand at the scale most backlogs require. But it also means the stakes of getting it wrong are high, because errors do not just produce a typo, they shape who gets to be seen in the historical record.
Where Archival Gaps Originate
Understanding the source of silences shapes where AI can help
- Acquisition choices that favored powerful institutions over everyday communities
- Description language and categories that erased or distorted marginalized experiences
- Processing backlogs that leave underrepresented collections undescribed and unfindable
- Catalog systems whose search defaults privilege already well-documented material
Using AI to Identify Gaps and Underrepresented Voices
One of the most promising applications is also one of the least visible to the public: using AI to understand what a collection actually contains and where its blind spots lie. Many institutions cannot answer basic questions about the diversity of their own holdings because the metadata to support such an analysis simply does not exist. AI can help build that picture by analyzing existing finding aids, catalog records, and digitized content to surface patterns, gaps, and imbalances that would take years to map by hand.
For example, natural language processing can scan thousands of descriptions to reveal which geographies, time periods, languages, and communities are well represented and which are nearly absent. Computer vision applied to digitized photographs can flag images that lack any descriptive metadata, prioritizing them for human attention. Entity recognition can extract names of people, organizations, and places buried in transcripts and correspondence, revealing networks and individuals who were never formally indexed. None of this replaces the archivist's interpretation, but it gives a small team a map of where the silences are so they can direct limited resources toward filling them.
This kind of analysis turns diversity work from a vague aspiration into something measurable and plannable. Instead of guessing where to focus, an organization can use the patterns AI surfaces to set collecting priorities, write targeted grant proposals, and explain to its board exactly where the collection falls short. The output is most useful when treated as a set of questions rather than answers. An apparent gap might reflect a genuine absence, or it might reflect material that exists but was described in ways the analysis could not recognize, which is itself a finding worth investigating.
What AI Can Help Reveal
- Imbalances in geography, era, language, and community across descriptions
- Digitized items lacking any descriptive metadata at all
- Names and places never formally indexed in finding aids
- Hidden connections between people, organizations, and records
Turning Findings Into Action
- Set collecting priorities grounded in evidence, not assumption
- Direct scarce processing time toward the most invisible material
- Strengthen grant proposals with concrete gap analysis
- Treat surprising gaps as questions to investigate, not verdicts
AI-Assisted Appraisal and Description at Scale
The largest practical opportunity lies in the slow, expensive work of description. Cataloging is time consuming and labor intensive, which is exactly why so much material remains undescribed. AI offers ways to create new layers of metadata at scale and for relatively low cost, opening collections that would otherwise stay closed for years. The most mature tools in this space address recognized bottlenecks rather than trying to replace the archivist's expertise outright.
Handwritten text recognition is among the most transformative. For decades, optical character recognition opened printed texts to full-text search, but handwriting remained a wall. Modern handwritten text recognition can transcribe manuscript letters, diaries, ledgers, and registers, and some platforms let institutions train models on the specific scripts and document types in their own holdings. This matters enormously for diversity, because so much of the documentation of ordinary and marginalized lives exists only in handwritten form, in personal correspondence, community records, and registers that were never typed up.
Beyond transcription, AI can draft descriptions for photographs and audiovisual material, generate captions, summarize lengthy recordings, and propose subject tags. These outputs are best understood as a first draft that a knowledgeable person reviews, not a finished catalog record. Appraisal, the judgment about what has enduring value and how it relates to the rest of a collection, remains firmly human work. Current tools lack the contextual nuance required for sound appraisal and cataloging, and human oversight is essential for ethical interpretation and for maintaining the provenance and authenticity of records. The realistic model is AI as a tireless assistant that drafts and surfaces, with a person who decides and corrects.
Handwritten Text Recognition
Opening manuscript material that search could never reach
Handwritten text recognition transcribes letters, diaries, registers, and ledgers into searchable text, and trainable models can adapt to particular hands, languages, and document types. Because so much of the record of marginalized communities survives only in handwriting, this is one of the clearest ways AI can widen who is findable. Transcripts should be flagged as machine generated and reviewed for accuracy, especially for names and places where errors do the most damage.
- Makes manuscript collections full-text searchable for the first time
- Trainable on specific scripts, languages, and document formats
- Output should be labeled as machine generated and reviewed
Image, Audio, and Video Description
Drafting metadata for material that has none
Computer vision can propose captions and tags for undescribed photographs, while speech recognition and summarization can make oral histories and recorded events searchable. For collections that have sat silent because no one had time to describe them, even a rough first pass can transform discoverability. The risk is that automated descriptions carry a narrow cultural lens, so review by people with relevant knowledge and community context is essential before such metadata is published.
- Draft captions and tags for photographs lacking any metadata
- Transcribe and summarize oral histories and audiovisual records
- Require knowledgeable review before publishing generated descriptions
Entity Recognition and Linking
Surfacing the people and places buried in the text
Named entity recognition extracts people, organizations, places, and dates from transcripts and descriptions, then links related records so a researcher can follow a person across a collection. This is where AI can knit together the fragmented traces of a life that an institution holds but never connected. Disambiguation remains imperfect, particularly for names from underrepresented cultures, so extracted entities should be reviewed rather than trusted as authoritative.
- Extract names, places, and dates that were never indexed
- Link related records so researchers can trace a person or theme
- Review disambiguation carefully for non-English and uncommon names
Description work also produces a wealth of institutional knowledge that is easy to lose as staff and volunteers turn over. Capturing decisions about how material was described and why pairs naturally with the practices in our guide to AI for nonprofit knowledge management, which helps small teams retain the context behind their collections.
Community-Sourced Collecting and Participatory Archives
The deepest way to diversify a collection is to invite the communities it documents to help build and describe it. Community-centered archiving has become an established practice, with institutions increasingly collaborating with underrepresented communities to preserve their histories on their own terms rather than imposing outside categories. AI can support this participatory work, but it cannot substitute for the relationships and trust that make it possible. The technology is a helper at the edges of a fundamentally human process.
In practical terms, AI can lower the barriers that often keep community members from contributing. Automatic transcription lets volunteers correct a machine draft rather than start from a blank page, which is far less daunting. Translation tools can help an archive engage contributors in their own languages. Tagging suggestions can help community members who are not trained catalogers add useful metadata to family photographs and documents. For small museums, community archives, and under-resourced organizations, lowering these barriers can be the difference between a collecting initiative that thrives and one that stalls.
The crucial principle is that communities should retain authority over how their materials are described and used. Participatory collecting goes wrong when an institution treats contributions as raw material to be processed by its own conventions, overriding the meanings and language that contributors bring. AI makes this risk worse if it imposes standardized tags that flatten cultural specificity. Done well, community-sourced collecting uses AI to remove friction while leaving interpretive authority with the people whose lives are documented, which both improves the quality of the record and builds the trust that sustains the relationship over time.
Supporting Participation Without Overriding Communities
How AI can lower barriers while leaving authority where it belongs
- Offer machine drafts that contributors correct, not blank forms to fill
- Use translation to engage contributors in their own languages
- Let communities define and keep their own descriptive language
- Avoid standardized tags that flatten cultural meaning and context
- Build the relationships and trust that AI alone can never create
Confronting the Risks: Bias, Provenance, and Consent
The case for AI in archives only holds if the risks are taken as seriously as the opportunities. Digital technologies both inherit and amplify the prejudices embedded in the analog records they process, and that bias can enter at every stage, from the data a model was trained on, to the way content is annotated, to the design of the algorithm, to how researchers interact with the results. An archive that deploys AI without scrutiny may find it has automated the very exclusions it set out to repair, only faster and with a veneer of objectivity that makes the bias harder to see.
Provenance is the second major concern. Archival value rests on knowing where a record came from, how it was kept, and whether it is authentic. AI complicates this in two ways. Generated descriptions and transcriptions become part of the record, so they must be clearly labeled as machine produced, with their reliability noted, much as some institutions mark AI-generated transcripts with explicit attribution. And the broader flood of synthetic content raises hard questions about authenticity that no single tool or standard resolves on its own. Maintaining trustworthy provenance increasingly requires deliberate practices and field-wide collaboration rather than faith in any vendor's claims.
Consent and control are the third pillar, and they are especially acute when records document living people or communities with histories of exploitation. Communities may have legitimate reasons to limit how their materials are described, accessed, or used to train external systems. A responsible posture treats sensitive material with caution, secures meaningful agreements about how data may be used, and retains the ability to stop a vendor from using collection data to train models the institution cannot oversee. Reading collections wholesale into third-party AI services, without such safeguards, can surrender control over irreplaceable cultural material in ways that cannot be undone.
Bias That AI Can Replicate and Amplify
- Weaker performance on non-English names, languages, and scripts
- Image descriptions shaped by a narrow cultural perspective
- Tagging that reinforces outdated or harmful historical categories
- A false sense of objectivity that hides where bias entered
Protecting Provenance and Consent
- Label machine-generated descriptions and transcripts clearly
- Document how each layer of AI metadata was produced and reviewed
- Secure agreements that bar vendors from training on your collections
- Honor community wishes about access, description, and use
Because so much archival material includes information about identifiable people, these concerns connect directly to broader data responsibilities. Our overview of data privacy and security for AI in nonprofits can help you set the guardrails that keep sensitive collection data protected when you introduce new tools.
Keeping Human Expertise and Collaboration at the Center
Across the field, the consistent conclusion is that AI does not correct bias on its own and that human intervention is critical. The most credible projects keep archivists, librarians, and community members in the loop at every meaningful decision point, treating AI output as a draft to be reviewed rather than a finished product to be trusted. This is not a temporary limitation that better models will erase. Appraisal, contextual interpretation, and ethical judgment are the parts of archival work that depend on understanding people and history, and those remain human responsibilities.
One of the most significant obstacles to ethical, inclusive AI in collections is the gap between the people who build these tools and the people who understand the records. When developers work without archival expertise, they tend to optimize for speed and volume while missing the nuance that determines whether a description honors or distorts a community's history. Closing that gap means archivists shaping how tools are configured, what they are trained on, and how their output is checked, especially for collections that contain sensitive historical material. Small organizations can participate in this by joining sector networks and sharing what they learn rather than facing the problem alone.
For nonprofit leaders, the practical implication is that adopting AI in archives is a change-management project as much as a technical one. Staff and volunteers need to understand both what the tools can do and where their judgment remains irreplaceable, so they neither dismiss the technology nor over-trust it. Building that shared understanding, and the internal advocates who sustain it, follows the same playbook as other AI adoption efforts. Our guidance on building AI champions within your organization applies directly to archives teams navigating this shift.
Principles for Human-Centered AI in Archives
The commitments that keep diversification work trustworthy
- Treat every AI output as a draft for expert and community review
- Keep appraisal and ethical interpretation as human decisions
- Involve archivists in how tools are configured and evaluated
- Collaborate across the sector instead of solving problems alone
- Build staff understanding so tools are neither dismissed nor over-trusted
A Practical Roadmap for Getting Started
You do not need a research budget or a data science team to begin. The most successful small archives treat AI adoption as a sequence of careful, low-risk steps, starting with a narrow pilot and expanding only as confidence and governance grow. The roadmap below gives a sensible order of operations for a cultural-heritage nonprofit working with limited staff and resources.
Step 1: Define the Diversity Goal and a Narrow Pilot
Choose one concrete outcome, such as making a specific undescribed manuscript collection searchable or mapping the gaps in your photographic holdings. A narrow, well-defined pilot lets you learn how the tools behave on your own material before committing to anything broad, and it produces a result you can show stakeholders.
Step 2: Assess Sensitivity, Consent, and Rights First
Before any material touches an AI tool, review whether it contains sensitive information, depicts identifiable people, or belongs to communities whose consent matters. Decide what may be processed, what must stay out of external services, and what agreements you need from vendors about training and data use. Getting this right at the start prevents irreversible mistakes later.
Step 3: Choose Tools That Fit the Task and Your Values
Match the tool to the job, whether that is handwritten text recognition, image tagging, or entity extraction, and favor options that let you review output, retain control of your data, and avoid using your collections to train external models. Established tools used widely in the heritage sector are a safer starting point than untested general-purpose services.
Step 4: Build a Human Review Workflow
Decide who reviews AI output, what they check for, and how machine-generated content is labeled before it reaches the public catalog. Pay special attention to names, places, and culturally specific descriptions where errors do the most harm. Where possible, bring in community members to review descriptions of material that documents their own histories.
Step 5: Document Provenance and Track Quality
Record how each layer of AI metadata was created, which tool produced it, and how it was reviewed, so future users and staff understand its reliability. Sample the output regularly to monitor accuracy and watch for systematic errors that affect particular communities or document types more than others.
Step 6: Evaluate, Share, and Scale Deliberately
Measure whether the pilot actually improved discoverability and diversity, share what you learned with peer institutions and your board, and expand only where the results and the safeguards justify it. Treat scaling as an ongoing cycle of evaluation and refinement rather than a one-time rollout.
Common Pitfalls to Avoid
Even well-intentioned projects can go wrong in predictable ways. Knowing the common pitfalls in advance lets you design around them rather than discover them after a community raises a concern or an inaccurate description spreads through the catalog. Most of these mistakes stem not from the technology itself but from treating it as a shortcut around human judgment and relationships.
Watch Out For These Mistakes
- Publishing AI descriptions without review, then propagating errors at scale
- Assuming automation is neutral when it can amplify historical bias
- Feeding sensitive or community-held material into external tools without consent
- Failing to label machine-generated metadata, blurring provenance
- Letting standardized tags override community language and meaning
- Surrendering data control by letting vendors train on your collections
Conclusion
AI offers cultural-heritage nonprofits a real chance to do something that has long felt out of reach: to make the hidden, undescribed, and underrepresented parts of their collections findable at last. Handwritten text recognition, image description, entity extraction, and gap analysis can each chip away at the silences that have made the historical record look more uniform than the world it documents. For a small archive overwhelmed by backlog, that capability is genuinely transformative.
But the technology is a tool, not a solution. AI does not correct bias on its own, it cannot make appraisal judgments, and it cannot build the relationships that participatory collecting depends on. Left unsupervised, it can replicate the exclusions it was meant to repair, blur the provenance that gives records their authority, and override the communities whose stories it processes. The institutions that get this right are the ones that keep human expertise and community authority firmly at the center, treating AI output as a draft to be checked rather than a verdict to be trusted.
Start small and start carefully. Pick one collection, sort out consent and provenance before anything is processed, build a review workflow with the right people in it, and measure whether the work actually widened who can be seen in your archive. Approached this way, AI becomes what it should be: a way to extend the reach of dedicated archivists and the communities they serve, so that more voices finally make it into the record they belong in.
Make Hidden Histories Findable, Responsibly
We help cultural-heritage nonprofits, libraries, and community archives adopt AI in ways that widen access while protecting provenance, consent, and the communities they serve. Let us help you plan a thoughtful first step.