this post was submitted on 20 Jul 2026
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The “failure mode” of AI editing is different though.
Humans (I guess) might mislabel something or take a bad shot. If they try to touch it up “traditionally” they could mess up the coloration at most.
But with AI editing, now you have to watch out for fine details you’d normally use for identification being completely, convincingly fabricated, as the article points out, with altruistic intent from the user (who’s just trying to submit data that looks alright)
The solution is global AI literacy; but that’s not going so well.
For "AI editing", I don't have much of a problem if a model was trained to tweak the settings in something like Darktable to achieve a good baseline to start with. However, for contributing to research like this, I draw the line when models start generating their own pixels and overwriting the original image.
Hopefully iNaturalist and other similar groups start to look at the metadata of submitted images to help warn/educate end users about this problem. That would at least help with the AI literacy issue. Those metadata tags are already being placed there by the most popularly used tools.
Yes, actually that would be great!
All this has happened so fast; it takes time to react I suppose.