Could FamilySearch use AI to generate family trees?

 

The idea of using AI to generate family trees for insertion into the FamilySearch.org Family Tree was raised in an interview during RootsTech 2026 of Elder Mark A. Bragg, a General Authority Seventy and executive director of the Family History Department of The Church of Jesus Christ of Latter-day Saints. See Could we ever run out of temple names? Elder Mark A. Bragg on why family history is accelerating.

The fact is that the FamilySearch.org website already has a section with computer generated family trees. You can find this section in the Genealogies directory under the main Search tab. 

This section contains seven different computer-generated trees, but no trees have been generated since 2023. Here is a screenshot of the current selection of files available to search. 

One of the statements made in the video cited above, is that AI is significantly accelerating family history by scraping data from digitized books and records to build out trees automatically. It is certainly true that AI does have the ability to build automatic family trees. However, the reality of creating "family trees" from raw data is not as simple as scraping a book. Despite the ability of AI to scrape a book and build a family tree, the actual reality of the process of creating a valuable genealogical resource is much more complicated. There are several tremendously difficult obstacles to any actual AI generated tree having valid information. None of which were mentioned in the interview above. 

Back in 1894, with the formation of the Genealogical Society of Utah, the predecessor of FamilySearch, the main concern was preventing the duplication of temple ordinances. See Allen, James B., Jessie L. Embry, and Kahlile B. Mehr. Hearts Turned to the Fathers: A History of the Genealogical Society of Utah, 1894-1994. BYU Studies, Brigham Young University, 1995. One of the key issues in the demise of the predecessor of the FamilySearch Family Tree (new.familysearch.org) was the proliferation of rampant duplication of the ordinances. My great-grandfather, Henry Martin Tanner, had his temple ordinances done over 800 times, despite the fact that he performed the ordinances during his lifetime in the St. George Temple.

With the current state of the FamilySearch Family Tree, the issue of duplication is by far the most serious. My own experience is that any time I do research for adding names to the FamilySearch Family Tree, I routinely find duplicate records. Taking a set of records from any major European country and including most of North and South America would produce a significant number of duplicate entries. If AI is able to create a valid family tree, then why not use it to eliminate the duplicates that are currently in the FamilySearch Family Tree? 

Another major issue with the FamilySearch Family Tree is the existence of "floaters," who are people and segments of family trees that are, in a real sense, floating around the main FamilySearch Family Tree because they lack any connections. Many of these floaters are duplicates and also are not currently readily available for inclusion, if they are discoverable at all, because of a lack of more specific information about their existence and identity. 

 An undercurrent of the concept of computer or AI-generated family trees is the issue of whether or not individuals, such as all of the currently active genealogists contributing to the FamilySearch Family Tree, have any future utility. If my job is going to be turned over to AI, then why am I spending time doing research and verifying names through careful application of the genealogical proof standard if it is only necessary to identify a name from a book to qualify for entry? Name extraction has, in the past, created a tremendous number of duplicate entries. It is still common on the FamilySearch Family Tree to find English records with duplicates because births, marriages, and burials were all extracted separately. A family with six or eight children could have thirty or forty duplicates. Current name extraction programs are also creating a substantial number of duplicate entries.

The current state of the FamilySearch Family Tree, where changes are randomly made to established entries without any supporting data, is also undermining the ability of the tree to function efficiently. Unless these systematic failings of the FamilySearch Family Tree can be adequately addressed, whether by AI or otherwise, it seems vastly premature to propose AI-generated family trees. 

 Since I now work full time essentially as a genealogist with the primary goal of finding and verifying additional people for the FamilySearch Family Tree, I would appreciate it if someone would let me know if it's time for me to retire.

T

Online with New AI Developments Every Wednesday (except for Holidays)

 

https://familyhistory.lib.byu.edu/get-help

The BYU Library Family History Center is the host of my weekly Understanding AI Q&A discussion on the Virtual Help Desk. We meet in a Breakout Room and talk through some of the weekly changes and I answer question. This is all totally free on the BYU Library Family History Center's open Zoom. 

The time is 10:00 am Mountain Daylight Time. Here is the link again to the Virtual Help Desk.

https://familyhistory.lib.byu.edu/live-virtual-family-history-help

The Virtual Help Desk is also open for consultation with all of the other missionaries on the shift at the time of your call. We also have a variety of specialists on countries and many other resources. 

The Coalition for Responsible AI in Genealogy in the Church News

 

https://www.thechurchnews.com/living-faith/2026/03/06/researchers-provide-guidelines-for-responsible-ai-usage-family-history-rootstech/

Although it kind of got buried in all the articles featuring RootsTech 2026 and other news, there was a good informative article about the panel discussion from the Coalition for Responsible AI in Genealogy or Craigen.org. Here is a short summary of the article. 

At RootsTech 2026, experts addressed the evolving landscape of family history by establishing a framework for artificial intelligence and sharing new methods for researching pioneer ancestry. A panel from the Coalition for Responsible AI in Genealogy, led by researcher Lynn Broderick, introduced five core principles—accuracy, disclosure, privacy, education, and compliance—to guide users through the technological "wilderness."

During the discussion, James Tanner cautioned against the Dunning-Kruger effect in AI, where the technology may generate false information when it lacks verifiable data. To counter this, David Ouimette emphasized the importance of skilled prompting and human discernment. The panel also highlighted the "Water Cooler Rule" regarding privacy, with Steve Little and Katherine Borges warning against uploading sensitive personal information or DNA data into AI systems where storage and usage policies may be unclear.

In addition to technological guidelines, the conference featured sessions on local history. Julie Merrill provided practical advice for navigating unorganized pioneer temple records, recommending the use of specific annotated records to bridge gaps in research. Further honoring the past, Ellen Jeppson shared stories of early women pioneers like Ruth May Fox, highlighting the 125-year legacy of the International Society Daughters of Utah Pioneers and its mission to preserve the faith and courage of those who settled the region.

There were also a few photos. 

Decipher your archive with Leo Technologies Limited

Leo

 

https://www.tryleo.ai/

Leo is a comprehensive digital hub designed to transform how historical manuscripts and archives are managed, offering an all-in-one transcription and document management platform. At its core is a state-of-the-art AI model specifically optimized for historical handwriting—such as secretary or cursive hands—and printed materials in English, Latin, French, German, and Spanish. Unlike other services, Leo is ready to use immediately with no model training required, though it maintains the flexibility to incorporate other AI models like GPT, Gemini, and Claude.

The user experience follows a straightforward three-step workflow:

1. Upload: Users begin by uploading images of handwritten or printed documents written in Latin scripts from the past 500 years.

2. Transcribe: The platform's machine learning technology generates instant, accurate transcriptions that preserve complex elements like margin notes, tables, and strikethroughs.

3. Manage: Once transcribed, documents are organized within a "Dropbox-like" interface. Users can categorize their collection into custom lists, utilize advanced search across all notes and transcripts, and add metadata for deeper filtering.

Beyond individual research, Leo emphasizes collaboration. Researchers can share their work through secure public links, allowing colleagues or students to access the materials and even copy documents to their own collections without needing a personal account. When the research is ready for the next stage, data can be exported in various professional formats, including XML, Word, or PDF.

To accommodate different project scales, Leo offers a tiered pricing structure:

• Free: Includes 10 monthly credits and storage for 100 images.

• Standard ($15/mo): Provides 100 monthly credits and storage for 10,000 images.

• Scholar ($65/mo): Provides 500 monthly credits and storage for 50,000 images.

• Professional ($120/mo): Provides 1,000 monthly credits and storage for 100,000 images.

For users with specific needs, Leo also sells additional lifetime credits that never expire, ensuring that even if a subscription ends, the research remains accessible and exportable.

Neo-luddites and AI: A Retrospective

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If you need a review of the history of Luddite movement, see https://en.wikipedia.org/wiki/Luddite. 

Let's start by thinking about photographs and digitization. The Kodak Professional Digital Camera System (a Nikon F-3 camera equipped by Kodak with a 1.3 megapixel sensor) for photojournalists was introduced in 1991. If my calculations are correct that was 35 years ago. Than means that would need to be about 45 years old to remember how quickly digital cameras replaced almost all film versions. Adobe Photoshop 1.0 was officially released on February 19, 1990, exclusively for the Macintosh. The internet, in the form of the World Wide Web, The first online version of the World Wide Web, created by Tim Berners-Lee at CERN in December 1990, was hosted on a NeXT computer at http://info.cern.ch. It functioned as both a browser and editor, showcasing the first website, which explained the WWW (World Wide Web) project itself and how to use hypertext. As a matter of note, I first viewed the WWW when there were six websites. 

Almost everything I read is currently digitized and primarily online. Every photograph that I take is a digital photograph. Since Google's nano banana was integrated with Adobe Photoshop, all the photos that I take that I use any manipulation to alter are done by using AI in Photoshop. All the major genealogical websites are now almost totally AI-intensive. 

OK, so here is the issue. I am not a regular reader of Facebook but from time to time, I do read a few posts. I find a very vociferous anti-AI minority, mostly teachers and academics. I even see statements about how the posters are never going to use AI. There are also statements about how easy it is to spot an AI photo or article. This may have been an issue two or three years ago but my own experience is to the contrary. You cannot tell an AI photo from one taken with a camera because both are essentially the same thing. Obviously if the image show a dancing hippo or a waltzing dinosaur, the images are most certainly generated. 

If you think you are never going to use AI, you may as well turn off your electricity and move out into a camp somewhere in what little wilderness is left in the world because, shortly, almost everything electric will have some AI function.

AI is a tool. You must use it as the master in a master/servant relationship. You make all the important decisions. AI is not a browser or a search engine, it is AI. Here is an example. The paragraph above beginning with OK was written entirely by me not using any AI assistance. Here is the same paragraph after I told Google Gemini to revise the paragraph for grammar, spelling, and accuracy. 

"OK, so here is the issue. I am not a regular reader of Facebook, but from time to time, I do read a few posts. I find a very vociferous anti-AI minority, mostly teachers and academics. I even see statements about how the posters are never going to use AI. There are also statements about how easy it is to spot an AI photo or article. This may have been true two or three years ago, but my own experience is to the contrary. You cannot tell an AI photo from one taken with a camera because, at the pixel level, both are essentially the same thing. Obviously, if the image shows a dancing hippo or a waltzing dinosaur, the images are most certainly generated."

I used the suggestions from Gemini to revise my original paragraph and the rest of the article.  

Now, the quote by Sam Altman, CEO of OpenAI: "AI won’t replace humans. But humans who use AI will replace those who don’t." Right now, if you don't know how to use AI, you don't know how to use FamilySearch, Ancestry, MyHeritage and most of the other genealogy programs. 

MyHeritage introduces color coding for family trees.

 

https://blog.myheritage.com/2023/03/introducing-color-coding-for-family-trees/

MyHeritage.com has taken another interesting step in organizing online family trees. The blog post, "Introducing Color Coding for Family Trees," details a significant visual and functional update designed to make family trees more intuitive and easier to navigate. This enhancement expands color coding—previously limited to Fan views and Timelines—to the primary Family and Pedigree views.

The update introduces a systematic approach to lineage, assigning distinct colors to specific ancestral branches:

• Blue: Paternal grandfather's side

• Green: Paternal grandmother's side

• Red: Maternal grandfather's side

• Yellow: Maternal grandmother's side

• Purple: Direct descendants

A key strength of this feature is its automation. Once enabled, the colors are applied across all family sites within an account without requiring manual input. The article notes that while the feature is currently available on desktop and mobile web browsers, it is slated for a future update to the mobile app.

Beyond the color scheme, the article highlights several complementary tools aimed at clarifying complex relationships. A new "blood relative" icon, represented by two drops, now appears in profile panels to help users identify genetic connections quickly. Furthermore, the update improves the experience of exploring trees belonging to DNA Matches or Smart Matches. By applying color coding and relationship diagrams to these external trees, users can more easily visualize how they are connected to individuals in trees they do not manage.

Ultimately, the article frames these updates as a cohesive effort to simplify genealogical research. By standardizing visual cues and providing clearer relationship paths, the platform aims to make the vast amount of data in a family tree more accessible and understandable at a glance.

Here is a screenshot of the color coding in the Fan Chart view. 

I use AI to summarize news releases and other routine communications.