From "Law and Order" to "CSI," not to mention real life, investigators have used fingerprints as the gold standard for linking criminals to a crime. But if a perpetrator leaves prints from different fingers in two different crime scenes, these scenes are very difficult to link, and the trace can go cold.
The research is bogus then as well. Projections like those have been in wide use at police stations around the globe at least since my cousin bragged about having this when he started his police training. That was.2008.
I mean, the research is the research and the data is the data.
If there are specific critiques to the methodology of the research that calls the validity of the observed data into question, that’s fair. “It’s ‘well known’ that…” Isn’t a scientific argument. It’s actually the exact opposite, it’s literally religion.
Also, the conclusions being drawn from the data by the researchers or 3rd parties might be a problem.
To be fair, ML of today is unrecognizable to what it was in 2008. And, I’d be willing to bet the model your cousin was exposed to wasn’t a machine learning model, and instead some handcrafted marker analysis with dubious justification but a great sales team.
The great thing about ML science is that it’s super accessible. This was an undergrad project. The next step, to establish the validity, really just requires a larger data set. If it’s bogus, that’ll come out. If it’s valid, that’ll come out too. The cost of reproducibility is so low that even hobbiests can verify the results.
My cousin wasn’t using any ML model. Their software probably did a geometric projection and that’s it. Then they’d search for the proposed owner of the fingerprint and get the real ones to compare against. That’s something that ML models cannot take from police as long as long as hallucinating is possible.
Right, so this methodology is a completely different approach. I don’t think it’s fair to call snake oil on this specifically with the justification that other models (using an entirely different approach) were.
Again, not saying it’s real or not, I’m just saying that it’s appropriate to try new approaches to examine things we already THINK we know, and to be prepared to carefully and fairly evaluate new data that calls into question things we thought we knew. That’s just science.