I was familiar with how their single-nucleotide polymorphism fingerprinting worked in principle when I submitted my sample. So, I was not surprised when my report indicated majority Native American (both my parents were born in the Navajo Nation).
As for preventing misuse of the genetic profile 23andMe built, the primary legal protection is the Genetic Information Nondiscrimination Act of 2008 (GINA) which prohibits insurance providers and employers from discriminating against patients and employees based upon disorders that are correlated with their genetic information. I believe it is prudent for people to examine their own genetic information in detail. I believe the legal protection GINA offers is sufficient for SNP profiling. I also believe as genetic profiling technology improves, the principles of non-discrimination set by GINA should be peotected with additional legislation.
The main issue I have as an editor is that there is no straightforward way to retrain the LLM to correct faulty training as directly or revertably as the existing method of editing an article’s wikicode. Already, much of my time updating Wikipedia is spent parsing puffery and removing phrases like “award-winning” or “renowned”, inserted by malicious advertisers trying to use Wikipedia as a free billboard. If a Wikipedia LLM began making subjective claims instead of providing objective facts backed by citations, I would have to teach myself machine learning and get involved with the developers who manage the LLM’s training. That raises the bar for editor technical competency which Wikipedia historically has been striving to lower (e.g. Visual Editor).