Just deleted my new “good idea”.
The problem was that the day this AI devil is out of the box in a confined social room like INat, every user will be involved to train it.
There is no “opt out” if it comes to peer review consequences. :-(
Most constructive idea: Give the money back?
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I don’t believe that peer review by AI is something to be taken as a serious scientific option.
If it shall be in future a “Facebook of living things”, than perhaps OK. For some applications (…may be most?), this makes a real difference. That’s what all the buzz is about.
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One of my more constructive thoughts so far:
For me, currently the process to initially assign an ID is pretty much OK.
The trouble starts with the review and the validation of what was found which relates to the identification content integrity.
Different people have much different habits (i stay positive 
) and there are a few enthusiasts who try to maintain data integrety by literally shovel tons of … every day.
Why not help them and provide better tools to data quality administration as a community task ?
#Idea:
-We have the reporters and co-reviewers proposed average taxon.
-We calculate from the already existing CV an average CV proposed taxon.
-We calculate the geometric distance between CV proposed and community proposed as a vector of divergence.
-We use gen AI to review the higest rank (most specific) taxon from either reporter or CV for criticality (#of reviewers, # of highest rank taxa considered, % disagreements, comment content, occurence of the taxon, direct or indirect observation evidence marked in comments) to assign it a commented criticality number.
-We scale the divergence vector (basically by some sort of multiplication) with the cricality number to generate a dynamic observation based criticality indicator
-We normalize that indicator to +10 (likely much overdetermined) to -10 (likely much underdetermined)
-We add this indicator to the observation and to the filter function as a dynamic number (dynamic because the average community rating will change over time)
-We keep the entire gen AI comments on criticality per default hidden to not pollute the human process part and thinking
-We make it visible only case by case on demand if somebody wants to understand why dog poo is considered a critical evidence to identify a dog.
Expected result:
PRO arguments:
-The non-expert will be able to understand his impact and can stay away from too difficult taxons but care for the data quality in most abundant and easy taxons
-The expert can focus onto cases where expertise is required.
-Both together can take their share to improve data integrity
-The criticality label of a find will dynamically reduce over time with the community adding more common sense to the original proposal
-Those seeking for details to start moer in-depth research may get it from the AI generated on-demand comments, based on that may contact other users or look into referenced material.
-People will engage more to use the annotations for to distinguish direct from indirect evidence to down-rank criticality, which makes a real difference (dont know how many hundred beavers are reported in Luxembourg just because somebody found a stick a beaver gnawed on 
.)
-If many people agree to abuse the system and call potential dog poo a scientific dog observation, it is still possible. But it will also be visible and they will discredit themselves visibly and automatically
CON arguments: ?
Maybe i was too technical and for sure, it is not thought through in all detail.
The rough idea from a community standpoint:
We have a very large and diverse network of HI (human intelligence) in the review process with all its capabilities and challenges.
I do not want an automated super-influencer of uncertain skill level (gen AI) who sets coordinated bias and interference to this HI network.
I want to see resouces (manpower, energy) spend the right way.
Proposal:
-Create a new gen AI supported metric to measure the quality of our activities in the process of identification based on the goals of this platform.
-This with no direct influence to the human thinking, only on explicit demand.
-Bring this quality metric to the level of “reviews accomplished” or “finds uploaded” so that people start competing for data and review quality rather than for mass of entries.
Expected result:
-Less reward for uploading minced meat finds and reviews
-Thereby reduce such entries in number, free-up workforce, memory storage, money and reduce energy footprint.
-By visualizing the quality metric in an intelligent way (above mentioned idea) as a criticality index give users a better chance to focus their review efforts according to their skills and knowledge level.
A friend of mine who is much more in this topic than i am said that this may not reflect the strengths of gen AI nor may follow the interest or intent of Google.
But i believe this is what we may need and want because it adresses many of the earlier mentioned concerns in this forum.
Just came about forum
https://forum.inaturalist.org/t/observations-by-suspended-users-should-indicate-the-user-is-suspended/67360/8
In amendment to above, the gen AI output related to a specific observation could also provide in addition a red-flag notice if a user was suspended. If it can digest user comments, that should be easy.
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I haven’t read the whole thread, so maybe this has already been said. For me the main thing would be that it stays within iNat.
- The AI only draws from sources in iNat (that is comments, journal posts and also linked articles) and not random junk of the internet.
- also doesn’t give it away. I suppose if google steals text, there is nothing we could do.
- give the sources, so one can check for themselves
- write the text in the language the page is set - comments are not necessary always in English
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Fair enough. There are AI applications which do this – for instance, in medical diagnostics, when an AI is trained to evaluate diagnostic imaging. This is, of course, predictive AI, more like the CV, rather than generative AI. Are there examples of generative AIs that remain within their specialized use-case platforms?
In some ways, one could question the relevance of even asking the question. AI ain’t going away, whether we find it “tolerable” or not. What technology that has found uses in the world has ever gone away because of people’s objetions? When robotics first entered the workforce, a lot of jobs were displaced; a lot of people understandably didn’t like that. But robotics are still here. A more extreme case: nuclear weapons. Yes, there are nonproliferation treaties, but nuclear weapons are still around. I don’t see any likelihood of AI going quietly into the night; especially as its energy efficiency improves and the energy tranistion proceeds, one of the main contraints on its application will abate.
Questions like this can be useful for ensuring that AI is deployed in the most appropriate way, minimizing destruction and maximizing benefit. But let’s not kid ourselves that a technology-based entity like iNaturalist will remain forever free of it.
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