Verifying Accuracy in AI Genealogy
Genealogy research increasingly relies on artificial intelligence software to parse historical records and construct family trees faster than humans can. However, just as AI models can amplify harmful biases or generate misleading text, AI genealogy tools carry the risk of propagating false connections if not designed carefully. So how can we build trust in this technology?
The core issue is the accuracy of an AI system's understanding - does it correctly interpret the subtle nuances in old census forms, birth records, and obituaries when assigning connections? The latest research in fact checking for misinformation and transparency in AI decision making can help.
By designing transparency into AI genealogy tools, we enable experts to audit the logic path and sources behind each connection made. If a system makes logically sound inferences backed by primary historical records, we can trust it where we would otherwise remain skeptical of an opaque black box. To communicate remaining uncertainty levels, systems could provide a confidence score to supplement family relationships, indicating where human review would help solidify more tentative links.
Beyond technical solutions, AI genealogy systems require the involvement of professional genealogists and historians in the development process, not just computer scientists. Domain experts ensure models represent nuanced best practices in assessing evidence sources and formulating hypotheses - skills not easily embedded in AI. Diverse viewpoints prevent blind spots.
If thoughtfully developed and responsibly deployed, AI-assisted genealogy research can greatly benefit family history exploration. But the risks inherent in AI mean progress requires cross-disciplinary collaboration, transparency, and a relentless focus on factual accuracy over speed or scale. With care, this technology may earn public trust and avoid undermining a pursuit based fundamentally on truth.