Ten Common Hallucinations in Genealogical AI

Ten Common Hallucinations in Genealogical AI

by Alvin Blackshear | Historian & Researcher <ablackshear@gmail.com>

If you've started using AI tools to help build out your family tree, you've probably noticed something exciting: AI can search patterns, suggest connections, and summarize records faster than any human researcher. But you may have also noticed something unsettling. Sometimes the AI is simply wrong, and it's wrong with total confidence. It doesn't hedge. It doesn't say "I'm not sure." It states fabricated facts as if they were pulled straight from a courthouse archive.

This isn't a quirk specific to genealogy tools. It's a well-documented feature of how large language models work. Researchers have found that AI systems are, in a sense, rewarded for guessing rather than admitting uncertainty, much like a student on a hard exam who writes down a plausible-sounding answer rather than leaving the question blank. The same dynamic that produces fabricated legal citations in court filings and nonexistent references in academic papers also produces invented ancestors, false marriages, and imaginary historical explanations in your family tree.

For beginning genealogists who are comfortable with AI tools but new to the discipline of genealogical research, this creates a real risk. Family history has its own standards of evidence (rules developed over generations of professional practice), and AI, for all its fluency, doesn't inherently know or follow them. Below are ten (plus a bonus eleventh) hallucinations you're likely to encounter, along with how to spot each one.

#1. Invented Ancestors

The most obvious and probably most common hallucination is the flat-out invented person. If your records show John Smith born in 1846, married to Sarah Jones, and died in 1912, an AI tool may cheerfully add "William Smith, father of John" to your tree, simply because a father-shaped gap exists and the model has learned that fathers usually appear in these slots.

No birth record names him. No probate record mentions him. No document anywhere establishes his existence. He is, in the most literal sense, a statistical guess dressed up as a fact.

How to catch it: Ask the AI directly, "What specific document supports this person's existence?" If it can't name one, or names one that turns out not to exist (see #5), strike the individual from your tree.

#2. False Parentage

This is a subtler and more dangerous error because it doesn't invent anyone; it just connects the right person to the wrong family. Imagine two men named James Brown, both born in Virginia in 1818, but belonging to two entirely unrelated families. An AI tool, pattern-matching on name, place, and approximate date, may merge them, attaching one man's documented parents to the other man's documented children.

Because online family trees copy from each other constantly, an error like this doesn't stay contained. It can spread across dozens or hundreds of public trees within months, becoming "true" simply through repetition.

How to catch it: Whenever two individuals share a name, county, and rough birth year, ask the AI to list the specific evidence distinguishing them: land records, church parishes, associates named as witnesses, distinguishing middle names. If it can't distinguish them, you probably have two people, not one.

#3. Collapsing Two People into One

Related to false parentage, this hallucination happens when the AI treats "same name, similar era, same county" as sufficient proof of "same person." Common names are especially vulnerable: John Williams, William Johnson, Mary Jones, Elizabeth Brown. In many rural communities of the eighteenth and nineteenth centuries, several unrelated people carried the same name at the same time.

How to catch it: Before accepting that two records refer to one individual, ask what independent evidence (land transactions, church membership, military service, or family testimony) ties them together, not just shared name and geography.

#4. Splitting One Person into Several

The mirror-image error: one real person recorded inconsistently, as "Robert," "Robt.," "Bob," and "R.J.," gets treated by the AI as four different people. Suddenly your family tree has artificially multiplied, with duplicate siblings or duplicate spouses cluttering the record.

Nineteenth-century clerks were not consistent spellers, and names were frequently abbreviated, translated, or anglicized. AI models, trained to treat text strings literally, can miss the obvious inference that a human researcher would make instantly.

How to catch it: When a "new" individual appears with a name variant of someone already in your tree, in the same location and timeframe, check whether the records could plausibly describe one person before treating them as separate.

#5. Invented Sources

Perhaps the easiest hallucination to catch once you know to look for it, and one of the most dangerous when you don't. AI tools will sometimes cite a source that sounds entirely plausible: "County Probate Book 14, page 327" or "Virginia Marriage Register, Volume 8." The citation has the right shape, the right format, the right level of specificity. It just doesn't exist.

This mirrors a broader, well-documented problem. A 2026 analysis of AI-generated academic writing found a sharp rise in nonexistent references following widespread adoption of large language models, with a conservative estimate of nearly 147,000 hallucinated citations appearing in 2025 alone. Similar patterns have shown up in legal filings, where courts have caught lawyers submitting briefs built on cases that were never decided because an AI tool invented them. Genealogy is not immune to the same failure mode; if anything, it may be more vulnerable, since archival citation formats are formulaic and easy for a model to imitate convincingly.

How to catch it: Never accept a citation at face value. Look the source up yourself, in the actual archive, library catalog, or online record collection, before recording it as fact. If you can't locate it, assume it doesn't exist until proven otherwise.

#6. Imaginary Historical Context

AI loves to explain. "The family likely moved west because of the Panic of 1837." It's a tidy, plausible sentence. It may even be historically reasonable. But plausible is not the same as documented, and an AI tool rarely marks the difference clearly. Historical interpretation, offered without evidence specific to your family, quietly becomes historical fiction wearing the authority of fact.

How to catch it: Treat any sentence containing words like "likely," "probably," or "given the context of the time" as a hypothesis to test, not a fact to record. Ask what document (a land sale, a migration record, a letter) actually supports the claimed motive.

#7. Overconfident Translation

If your ancestors left records in German, Polish, Latin, French, or Dutch, AI translation can feel like a superpower, until it isn't. AI models translate old church and civil records with impressive fluency, but that fluency can mask serious errors: invented words to fill illegible gaps, misread names, or entire phrases reconstructed from context rather than actually read. Genealogists working with foreign-language parish records have reported AI tools fabricating names, places, and even whole narrative details that don't exist in the original document.

How to catch it: For any translation affecting a name, date, or relationship, ask for a word-by-word rendering alongside the smooth translation, and compare it against the original image yourself, or have a human speaker of the language check ambiguous passages.

#8. Fabricated Relationships

A record lists a witness, Samuel Jones, at a wedding or a will signing. The AI concludes he must be an uncle, brother, father, or cousin. But witnesses in historical documents were frequently neighbors, business associates, fellow church members, or simply friends, not relatives at all. The AI's eagerness to complete the family picture leads it to assign a relationship where the record specifies none.

How to catch it: Treat every unlabeled name in a record (witness, executor, sponsor, bondsman) as a relationship of unknown type until independent evidence establishes otherwise.

#9. Timeline Repair

Large language models dislike contradictions, and historical records are full of them. If a record shows a birth in 1820 and a marriage in 1832, and something else nearby seems inconsistent, an AI tool may quietly adjust one of the dates so everything appears internally consistent. The apparent contradiction vanishes, but so does the actual historical evidence, replaced by a smoothed-over fiction that fits the model's sense of what "should" be true.

How to catch it: When a date changes between one AI response and another, ask why, and check both figures against the original record. Real historical records are often genuinely inconsistent, and that inconsistency is itself information, not a bug to be silently fixed.

#10. Source Laundering

This is probably the least recognized hallucination, and arguably the most insidious. AI tools trained on the vast ocean of user-submitted online family trees absorb whatever errors already exist there, including all the mistakes described above, made by human researchers over the past two decades. The AI then presents those errors back to you as though they were settled historical fact.

Nothing here is invented from scratch. That's what makes it dangerous. The information already circulates online, attached to seemingly credible trees and family history websites, so it carries an unearned appearance of authority. The AI hasn't lied exactly; it has simply repeated community folklore as though it were consensus.

How to catch it: Be especially skeptical of any claim the AI attributes to "commonly accepted" genealogy or "widely documented" family history. Ask what primary source underlies the claim, not what other online trees say about it.

An Eleventh Hallucination: Certainty Inflation

There's one more pattern worth naming, even though it's rarely discussed: certainty inflation. Professional genealogists are trained to say, when the evidence doesn't fully support a conclusion, "the evidence is insufficient." AI tools are much less comfortable with that kind of humility. Instead, they tend to write things like "based on available evidence, John was almost certainly the son of..."

That phrase, "almost certainly," can represent an enormous leap well beyond what the underlying evidence supports. This isn't accidental. Research into why language models hallucinate has found that the training and evaluation processes used to build these systems reward confident, plausible-sounding answers over honest expressions of uncertainty, the AI equivalent of a student guessing on a test rather than leaving a blank. The result is a model that is statistically inclined to sound more certain than the facts warrant.

Toward a Genealogical AI Evaluation Framework

None of this means AI is useless for family history research; far from it. It means AI output needs to be treated the way a careful historian treats any single source: as a claim to be verified, not a fact to be recorded.

One useful discipline is to treat every AI-generated genealogical statement as a research hypothesis, never as evidence in itself. Accept only what can be traced back to an identifiable primary or reliable secondary source, and preserve uncertainty wherever the historical record itself remains uncertain. In practice, that means running each AI claim through a short mental checklist:

Criterion	Key Question
Source authenticity	Is every factual claim traceable to a real, locatable source?
Evidence chain	Can the conclusion be reconstructed from the cited evidence?
Identity confidence	Has the AI distinguished between similarly named individuals?
Chronological consistency	Are dates plausible without silent correction?
Geographic plausibility	Do locations fit historical boundaries and migration patterns?
Uncertainty disclosure	Does the AI distinguish evidence from inference?
Genealogical Proof Standard alignment	Would the conclusion satisfy professional genealogical standards?

Applying a framework like this won't eliminate hallucinations (no current AI system is free of them), but it will keep you from mistaking a fluent, confident answer for a well-documented one. AI can be a genuinely powerful research assistant for genealogy: it can suggest search strategies, summarize long documents, translate unfamiliar scripts, and spot patterns across large record sets faster than any human could alone. Just remember that its job is to help you find the evidence, not to become the evidence itself.

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Sources:

Kalai, Adam, et al. "Why Language Models Hallucinate." arXiv, 4 Sept. 2025, https://arxiv.org/abs/2509.04664.

Newsham, Jack. "AI Hallucinations in Court Documents Are a Growing Problem, and Data Shows Lawyers Are Responsible for Many of the Errors." Business Insider, 27 May 2026, https://www.businessinsider.com/increasing-ai-hallucinations-fake-citations-court-records-data-2025-5.

Yin, Yian, et al. "LLM Hallucinations in the Wild: Large-Scale Evidence from Non-Existent Citations." arXiv, 8 May 2026, https://arxiv.org/abs/2605.07723.