I was ambivalent at first about the importance of opt-in vs opt-out, but between @iisips 's comments here and my own concerns about trolls, SEO bots, and misguided novices - any such algorithm should be:
Letās say one day a non-expert iNat user decides to look through all red-shouldered hawk observations with comments and make a list, in his/her own words, of what he/she considers to be characteristics useful for an ID. They then decide to post that list of characteristics, together with an indication of the source observation, on the forum, thinking it could be useful to other users. That list would probably look very much like the list extracted and compiled āartificiallyā (in other words, with the help of AI) and as the user is not an expert in IDing birds, it might very well contain āmistakesā very similar to (or maybe worse) than the ones made by the AI.
Neither the human intelligence (iNat user) nor the artificial intelligence is claiming the comments as their own, so I personally donāt believe it could be considered as plagiarism. Both cases could also include a disclaimer stating the list might contain errors of interpretation and both provide access to the original text via the observation, enabling all the information to be checked directly with the original source.
Letās say for the sake of argument, that both lists contain the same information and the same non-expert errors of interpretationā¦ what Iām genuinely trying to understand is whether the non-expert human generated list and the artificial intelligence generated list would both be equally upsetting? Or does the use of AI make the results intrinsically unacceptable, even if the outcome is the same?
I suspect that the heart of the issue is about consent. Consent is built on trust.
A computer program can āask for consentā, in a legal-technical fashion, but itās difficult to trust a corporate entity. They donāt take ānoā for an answer, or they pretend they did and lie about it. Yes, there are exceptions, but the rule still stands that a computer program is not a person with emotions, ethics, etc.. A computer cannot be held to account, and its programming can be changed at any time by nefarious outside forces. From a legal standpoint we have to grant our consent dozens of times a day merely to exist online ā but is it real consent? Or consent under duress?
I trust iNat to be better than that, of course, but not because I put any trust into an algorithm ā rather because I hope that while the team is creating, managing, and defending that program, they will respect moral principles and will respect usersā consent.
Consenting to a human doing something is an entire world of difference. Iām willing to trust that people, in general - and especially scientists or students - are inherently curious and well-meaning. Theyāre a person I can talk to, rather than a mysterious box of circuitry (or worse, a mysterious box of circuitry that puts on a human face). And if it goes poorly, humans can be held accountable. They feel shame when theyāre called out, or at least suffer social consequences when they cross boundaries and break consent. In extreme cases, the law can get involved.
To use an analogy, in my opinion this is a bit like this: say I created a painting. A kid takes a picture of my painting because they love it, and draws or traces an exact copy to put on their wall. Simultaneously, a large corporation makes an unauthorized scan of the painting, prints industrial quantities of it, and hands them out to anyone and everyone with no credit or indication that they did not originate the work. There is a difference in these two scenarios wouldnāt you agree? Scale, intent, credit, these all play a role in how the creator would feel about this. And the law agrees with me, one of these is fair use and the other is not.
And even if you ask it to credit specific indvidiuals when citing them, weāve seen just how that would turn out in this very thread. It confidently ascribes sentences and sentiments to people who never even said them. What if the AI leaves a tip on an observation that says āAccording to user hawkparty, you can identify this mushroom by its light color and pleasant tasteā, and the poster gets hospitalized because it was actually a poisonous mushroom. Am I liable now? Do I have to worry about getting sued? These arenāt things I want to have to worry about on a website I use for fun.
In addition, this reminds me of a conversation I had with a colleague when Copilot and other similar ācode generatingā AIs were becoming a topic in my professional field:
He said that in his opinion, the code that the AI generated was about on par with the work of an intern or a particularly green junior programmer, in that it needed some work to be made functional but could be used for basic tasks. The difference, according to him, was that you hired interns and juniors with the expectation they would learn and grow. You donāt hire them for efficiency, a senior engineer could write all the same things twice as fast and without the need to clean up and fix the work, the reason you hire them is because over time they improve, they take feedback from the seniors, and they accumulate knowledge, so that they can begin to tackle more complex tasks and even start designing and architecting, two things which GenAI has remained absolutely abysmal at. They eventually become senior engineers who can then train more juniors in the same way, thus is the cycle of tech dev.
The AI was, in the exact words of my college, āan eternal juniorā. It never learned or improved and it didnāt take feedback, it just kept generating the same kind of crappy code that required more work to clean up and fix. There was no point to it when plenty of juniors already exist(and that it takes away from their opportunities. If the AI is doing all the junior work, how are they supposed to get experience to become seniors?). The reason some companies have gone ahead with implementing these is because of the rather straightfoward concept that you have to pay juniors and you do not have to pay the AI. So itās cheaper to have the AI churn out the work of a couple dozen juniors and then have one senior engineer paid to fix it.
However in iNaturalistās case Robo here was already providing their work for free. And they arenāt compelled to continue offering it. If they decide to leave, there is no senior on the payroll who can fix the AIās output. Itās just going to continue spewing incorrect and poorly sourced misinformation without ever learning like a person might and nobody will be around to correct it.
Also, in the case of people, they usually have some unique perspective or ability they bring to the table. Maybe that new identifier speaks a language Robo doesnāt, so they can translate the tips to reach more people. Maybe Robo points them in the direction of some of the sources they use and that identifier discovers something new thatās been overlooked. And in time they can become a new experienced identifier that also provides knowledge to others. If these people leave, regardless of how āvalidā you think their reasons for doing so are, you get the āgarbage in, garbage outā problem. The GenAI continues to produce slop because all the skilled users have removed their data from the training pool and left, and in turn all it has to take in again is its own slop. You can imagine the end result.
I see a lot of AI enthusiasts constantly espousing that it doesnāt matter if people donāt like AI, because the AI can just replace them. But what they miss is that without these people, the AI has nothing. It is trained on their work, all it can do is reproduce it. The feelings of these people matter because as they leave, as they remove their data from the internet or refuse to publish it so that it can be scraped, all the AI can do is continue to subsist off its own slop. You canāt make a device reliant on the labor of others and then claim that it doesnāt matter how they feel about it because you can just replace them. iNaturalist should take note of that.
(And, only marginally related, I want to bring back my liability questions. Disney has recently initiated a massive lawsuit against GenAI company Midjourney. Does iNaturalist really want to open itself to this kind of legal action? Even if they end up being validated by a court of law, thatās a protracted legal battle that I frankly doubt iNaturalist can afford)
Given the number of posts in this thread, Iāve given it a week before commenting here hoping that most of the āsteamā has been blown off by now. I have tried to read through it but probably just glossed over a lot of it. Like several other people Iām surprised by the overwhelmingly negative reactions to the news. I do not envy the iNat team the task of trying to sort through and address all the concerns being voiced. It must feel like theyāve accidentally tripped and fallen into a hornetās nest.
1 - This is going to be long, so Iām going to sort it roughly into three topics, hopefully not repeating too much of what has already been said above. First, Iād like to point out how the AI risk debate and political decisions regarding regulations differ significantly in the US vs. Europe. This is probably something to be aware of going forward with any sort of AI applications. Preparing to teach a discussion class on genetic engineering, I can see a lot of parallels with the anti-GMO debate, e.g.:
To illustrate that last point, foods containing GMO products have been on the market for decades but werenāt required to be labeled (as containing bioengineered food ingredients) in the US until 2022. They are still prohibited in many countries in Europe. Sometimes when we talk about this in class we refer to it as āthe great American GMO experimentā and there are always sentiments that consent to be part of it was overruled by political and economic interests.
Similarly, internet users in the US currently have their social media content scraped by AI, often without their knowledge or consent, and are unwittingly/unwillingly becoming part of āthe great American AI experiment.ā For example, German news raised awareness of the possibility to object to having your public Meta (Facebook, Instagram, WhatsApp etc) posts used for AI training. I was able to find the corresponding URL in the privacy settings for my US Facebook account but it just said: āThis form is only available to people in certain regions.ā US users have all been opted in to AI training by default.
I expect these differences in AI consent and privacy options between the US and other places are going to become even more pronounced in the near future with diverging regulations driven by political and societal differences. They definitely feed the āgreedy corporationā and ābig brotherā vibe of this technology in the US. Itās no wonder many people are wary of AI being forced on them in a way that constitutes a power grab by big corporations and leveraging it for political gain.
Operating from the US but with an international user base, I think it will be necessary for the iNat team to be aware of these differences and concerns. In some other parts of the world, information considered a āfree-for-allā in the US might be protected by law. As already suggested, be transparent about its use on the platform and provide options for people to give consent to having their data used.
2 - Secondly, Iād like to reflect on the benefits of keeping at the forefront of AI research and development. Iām sure this is really what the demo to be developed with the help of the grant is about: Creating new tools (rather than applying existing ones) to make iNat even more useful for human users. As several people on this thread have done, anyone can already feed a bunch of comments into ChatGPT to generate a summary. That doesnāt require a grant to do and yes, the output will likely be flawed. Could it be improved? Maybe - or is there an entirely different way of approaching this? Grants are usually for thinking outside the box and developing something new that doesnāt exist yet. That is the whole point of research and development.
We are living in the Information Age where available data is still growing exponentially. AI is a game-changer in information processing, no doubt about that. I still remember as a graduate student taking pen and paper and a genetic code table to manually translate the ~200 nucleotides I read off the autoradiograph of my sequencing gel into amino acids so I could type those into the BLASTP algorithm to compare against the GenBank database on a set of CDs (no internet). Gosh, how things have changed! Iām thinking of all the DNA sequence data generated in our current lab projects and how weāre using AI to generate R scripts to analyze all that data and put it into diagrams and figures. Iām glad AI can help us with that!
Generative AI is inevitable at least in my job today and refusing to engage with it might backfire. E.g. our students have already wholeheartedly adopted it to help them with their homework assignments. Suddenly instructors are faced with having to at least address AI in the classroom. None of us opted in to this, it just became reality. Our students will likely need to know how to use AI for their future careers. A lot of us are taking workshops, playing catch-up. To quote Will Rogers: Even if youāre on the right track, youāll get run over if you just sit there. We could debate whether using AI is the ārightā track but currently thatās the track weāre sitting on. It will definitely be a subject of critical discussion about its uses and pitfalls as well as applied in the classroom in the courses I teach this coming fall.
One thing Iāve learned so far is that AI applications can be customized specifically for the task you want them to perform. The digital version of the textbook now comes with an āAI tutorā that was trained to apply the Socratic Method, replying to student questions with more questions prompting them to engage with the material and find the answers in the book rather than giving them a straight answer. Some of us are already using AI trained to write exam questions based on class materials or categorize written feedback from students in large enrollment classes. Generative AI can take many forms, itās up to us to tailor it to our needs and understand its limitations. Iām curious to see the demo the iNat team will come up with!
Based on how frequently my students use it, I think itās safe to say AI is here to stay. On iNat, I see a lot of people using the CV suggestions, especially those vast numbers of infrequent users who just want to put a name on things. I see this crowd of casual users as a prime target for the proposed tool. Maybe that could even lead to more public awareness of the community providing identifications on iNat. And Iād much rather see that effort be spearheaded by the iNat team than anyone else.
3 - Which brings me to my third point: I have confidence in the integrity and skills of the iNat team to use the grant money to further iNatās mission and that they wonāt compromise or sell out to ābig tech.ā My confidence is rooted in the fact that iNat spent its incubation time sheltered within academia rather than as a start-up funded by venture capitalists and was converted into an independent non-profit organization rather than being sold off to the highest bidder. Iām also thrilled that the original founders stuck around on the team and obviously care a great deal about iNatās mission and community, as well as use the site themselves. I appreciate the level of engagement with the community. Thatās a big difference to some other sites that have gone down.
Community and communication is really key to sites like this. My online photo sharing story started on Flickr, where Iāve experienced firsthand what happens when big companies take over an online community and prioritize profit. As the saying goes, on the internet if youāre not paying for a product you are the product being sold (typically to advertisers). The years when Yahoo redesigned Flickr and converted it into a free-for-all photo storage site that was supposed to be generating enough ad revenue to keep an unsustainable business model afloat pretty much turned a once thriving community into a ghost town. My first actions on iNat were to import all my nature photos from Flickr in case it goes down for good and forever.
Well, Flickr is still around today and I dare say experiencing a bit of revival within the photographer community. What happened? It was sold it off again to a company experienced in catering to photographers and thus familiar with their needs and expectations (SmugMug). It regained its focus on paid photo sharing. I believe the reason Flickr still exists is because the people now in charge of it care about its community of photographers and provide them with the tools they want and are willing to pay for. In parallel, iNat is still in the hands of people who are passionate about connecting people with nature, without the painful detour of being bounced around between businesses primarily interested in ad revenue that Flickr took.
Unlike Flickr, iNat is not tied to a business though and there are no ads or paid accounts, just an occasional ask for donations to help keep the servers running. This brings me to my last point, which is the need to pull in grants like this. Anyone remember ARKive? It went offline in 2019 due to lack of funding. Non-profits like this absolutely depend on funding in the form of grants and donations. So Iām thrilled to hear that iNat was able to secure another grant! I have no doubt that the iNat team will make the best use of it while keeping the concerns of the community in mind.
Thatās called plagiarism.
In case you hadnāt noticed, when we quote someone here in the Forums, their username and avatar appears at the top of their quoted words. You can see yours in the part of this post that quotes you.
Didnāt we all learn about plagiarism in school?
replying to express that i feel the replies by @astra_the_dragon and @hawkparty get around some of my thoughts here. i disagree about the point of plagiarism as i see important reasons for citations to not be mere lists of links- but that is not the major focus here so letās put that aside. let me attempt to explain the difference i am seeing here.
āLetās say one day a non-expert iNat user decides to look through all red-shouldered hawk observations with comments and make a list, in his/her own words, of what he/she considers to be characteristics useful for an ID.ā
(emphasis mine)
the hypothetical individual is applying their perspective in using their own words and weighing what they consider useful for ID. as an individual, they approach the task with their own experiences and biases: the way they transform the source material into their own words and what they weigh as important is going to differ from another individual. the output will have their fingerprints on it. they are responsible for the creation of that output and answering for what it contains. they individually own the process, what they learned from it, and the product.
but what of the generated output of a LLM? it is by its very nature Not an individual. we cannot challenge it to explain its interpretation or considerations because it did not interpret or consider things the conscious way we do. when you ask it to justify itself, the output of its answer is another blackbox process that we cannot verify accurately represents its prior āthought process.ā you could have it repeat the process again and again and it has no responsibility to be consistent. it has no perspective. it has no context besides your prompts. it has no responsibility to learn- users must bear the responsibility of dealing with its repeated errors.
but it all continues to spiral in to the central question of why?
i have expressed already that i would rather have the ability to write this summary myself in a form accessible by the community. the use of LLMs/genAI removes me from the discussion in a way i do not enjoy. i want it to be abundantly clear that i love learning, i love my subjects of interest, i love engaging with others while they learn and perhaps love the same subjects. by discussing with others of any skill, i am considering their individual perspective. this pressures my development too as i consider what they see, what they interpret, what they weigh as important.
it isnāt that i fear being replaced by LLMs/genAI. i do not believe i can be adequately replaced by these tools. instead i fear people looking at the outputs trained on my effort next to my original effort and judging it as āsame thing.ā an LLM/genAI process can do its damnedest to mimic me but it isnāt me. and i want to be me. i want to be me and a part of the process because i derive joy from participating and being in the community. i beg not to be shut out of this.
iām sorry. iāve deleted my earlier post.
ā¦
just for context, here are relevant points from that deleted post, with a bit of redaction:
i wanted to see how an AI might handle providing sample observations along with each feature. so i extracted XXXX notes from the 10000 most recent [taxon] identifications. again, this browser version of Copilot limits my input to ~10000 characters. so i fed it [a smaller portion of] of the XXXX notes and gave it this prompt:
using only on these identification notes for [taxon] from observations (obs) in iNaturalist, find relevant features that may be useful for identification, and provide up to 3 example observations for each feature:
and hereās how Copilot (based on a descendant of GPT 4) responded:
Hereās a structured summary of useful identification features for [taxon], along with example observations:
Identification Features & Example Observations
- [Feature 1 (3-4 words)] ā [brief elaboration (5-11 words)]
- obs 111
- obs 222
- obs 333
- [Feature 2 (3-4 words)] ā [brief elaboration (5-11 words)]
- obs 444
- obs 555
- obs 666
[ā¦]
- [Feature 10 (3-4 words)] ā [brief elaboration (5-11 words)]
- obs 777
These features provide strong identification points for distinguishing [taxon]. Let me know if you need further refinement!
i can imagine that instead of providing example observations as a bulleted list like above, iNat could present a series of small photos from each of the example observations, and users could click on interesting ones to see the observation details.
ā¦
thereās already been thorough discussion of many of the points youāve made. so i wonāt try to add to most of it. but i do want to expand on a few things:
consent.
to some extent, i think itās important to judge the AI (the tool) separately from the how itās used. itās true that AI can be used for bad (or poorly), but it can also be used for good (or well). here, i knew that folks were concerned about topics related agency and consent, and yet i produced my example in a way that was insensitive to those concerns. so i own that mistake. it was my own poor judgment / usage, and iām sorry.
credit.
here, again, i think itās worth judging the technology itself separately from its use. in my example, i explicitly asked the AI to tie features to observations. i purposely made that choice in an attempt to avoid earlier concerns about bad information or out-of-context information being tied to indviduals (causing reputational harm or exacerbating misinformation). one of the things i was testing in my example was whether AI could tie concepts to sources at all. as far as i can tell from this this and other private testing, thereās no reason to believe that an AI wouldnāt be able to refernce individuals and observations, if it had been provided such information and been asked to do so. unfortunatlely, my attempt to avoid one problem led to another. not crediting individuals in my example is my mistake, and iām sorry.
voice.
i think there are many ways to think about āhaving a voiceā. one of them is adjacent to consent, and i think another could be related to credit. so i wonāt try to address those again. there are other potential meanings, but one that i specifically want to address is related to how an idea is expressed (which iāll just call āexpressionā for short). i do think appropriation of expression is something that is a challenge inherent to generative AI technology itself, and one of the things iām interested to see in the demo is whether it can satisfactorily mitigate that sort of problem.
at some level, there are only so many ways you can express a factual concept, especially when that expression is done in fewer words (ex. ācardinals are red or brownā vs ācardinals are red (male) or brown (female)ā vs āadult male cardinals are brilliant red, while females are a duller brownish-grayā). so maybe one way to mitigate the expression problem is to limit the length of output, at the sacrifice of context.
another way could be to present only the bits of information (ex. individual identifying features) that, for example, had been conveyed by a minimum X number of users and / or by limiting any single personās input to some X% maximum of the total input for any given concept. i think the tradeoff there could be the exclusion of certain uncommon but interesting characters (ex. iāve noticed in my private testing that my AI will include things like examples of aberrations or examples of signs or other things that arenāt identifying features exactly but are adjacent) and also the potential exclusion of information about taxa where only a single helpful expert exists. to me, cases of the latter are sort of the gems in the iNat mine. i can envision an opt-in being useful here to allow those gems to be mined in cases where they otherwise would not be, although i can also see such an opt-in option being abused, too.
anyway, thatās a long way of getting to the idea that itās probably possible to mitigate appropriation of expression by generative AI, but i didnāt try hard enough to do that in producing my example. so iām sorry for that, too.
Thanks to you all for your thoughtful answers, I believe I now have a much better idea of your doubts and concerns.
This morning I woke (too) early, so just for fun I got āchattingā to CoPilot about the differences between human and artificial intelligence. I wonāt go into the details otherwise I risk triggering an off-topic debate, but I believe two of the AIās conclusions to be worth quoting (my bold):
AI ācan process massive amounts of information quickly, find patterns people might miss, and learn from data. [ā¦] When paired with human values and oversight, AI has the power to solve complex problems and open up new possibilitiesā
āWhile AI offers tremendous promise, it also demands thoughtful governance, ethical design, and public awarenessā.
That is exactly how I see it. AI is (for me) an extraordinarily powerful tool which (like any tool) can be used well or badly, for good or for bad. The issue (for me) is not whether I trust that tool, but whether I trust the people responsible for the conception, design and implementation of that tool. In this particular case, I have absolute faith in the iNat team to do everything in their power to implement āthoughtful governance, ethical design and public [the full iNat community] awarenessā.
Unfortunately I have absolutely no relevant skills and am far too old to learn them, otherwise I would find it absolutely fascinating to work on this demo project (if it goes ahead after so much negative reaction). But obviously thatās just my very personal opinion.
What would it mean for an opt-in option to be āabusedā? How could someone choosing not to participate in something be āabusiveā?
you need to read the whole thought, not just the part that you quoted. the opt-in option contemplated in that thought is a very specific one that would allow folks to bypass potential safeguards. the potential for abuse exists not in the absence of invoking the opt in. instead, it exists when opting in because those potential safeguards would be removed.
Most of the time (maybe all of the time?), when I make a comment about identifications on an observation, I am using what I learned from a printed field guide or a class I took or a website, but usually Iām not crediting that book or teacher or website. Should I start doing that? Perhaps something like, "In eastern North America, there are two native species of elderberry recognized in iNatās taxonomy: Sambucus canadensis and S. rubens. They are most easily distinguished by the shape and timing of their inflorescences, and by the color of ripe berries. See Go Botany for more details (but be aware that Go Botany lumps European and American Black Elderberries, unlike iNat): https://gobotany.nativeplanttrust.org/dkey/sambucus/ " Or should I just agree with the correct IDs, correct the wrong IDs, and get through quickly the dozens of Sambucus observations that get added every day at this time of year? I do the latter, obviously, unless someone asks. I think thatās the path taken by most identifiers, but should we all be citing our sources?
May I ask for clarification on what this āopt-inā option would be? Usually when people talk about having something be āopt-inā, they mean an individual can choose to have their data included, and if they donāt choose to do that then they arenāt included.
Would that be whatās happening here? To me it sounds like what youāre describing is an option that would bypass any safeguards like āa single person can only make up this percent of the training dataā to allow comments to be used in cases where there arenāt many identifiers. I think if thatās the case, the confusion over the option being abused is because the term āopt-inā usually has a different meaning for most people when talking about opting into AI training on their data.
seems like you generally understand what i wrote. it would bypass specific safeguards, not any safeguards.
is it not accurate to label what i described as an opt-in option? what is a better label for it?
It is an opt-in option, since itās opting into allowing your data to be used in a certain way. I was mostly meaning that because for a lot of the discussion here people have been wanting the feature as a whole to be opt-in, in other words not having their data be used at all unless they choose for it to be used that way, some people might see that term and assume thatās whatās being talked about. It admittedly took a few rereads before my brain really pieced together what was being opted-into, hence wanting to clarify that I wasnāt also misunderstanding.
I can see what you mean about it this specific kind of opt-in feature potentially causing issues (for example with particular identifiers making up the majority of the training data). I think once again though, if you end up with a taxon where there are only a handful of dedicated identifiers who would end up being almost the entire training dataset, it might just be easier to have something compiled their comments and present them as is, or give them an option to write something that can be put on the taxon page. A lot experts who study very niche subjects are delighted to share their knowledge and will gladly explain things to people who ask.
This is exactly why I donāt need to be credited for any comments I make, which are a synthesis of all my reference materials, not my own intellectual property. Of course I am prepared to provide sources, should an observer ever ask where I got my info (which I believe only happened once.)
Rather than any concern about not being given credit, I am far more concerned about false info. being wrongly attributed to me, a concern others have already expressed.
Hereās a question which reveals the depth of my lack of knowledge about AI:
Is it more likely to hallucinate in the absence of actual data to scrape? For taxa that donāt have many observations, or many observations with comments, is it more likely invent stuff? If it is designed to produce output, will it just do so by any means? (Like a student who is told they have to write three pages on how they spent their summer vacation, when all they did was play video games all day, so they invent things the teacher might like to hear, like books read and trips taken.)
This would be big problem, especially if good observers and identifiers delete their accounts, taking their collective wisdom when they leave.
I respect every userās right to do that, but I hope that however this all plays out, we donāt see a loss of knowledge that outweighs any potential gain.
i hope this is not too aside the point in the thread- it relates to the importance of sourcing and how GenAI makes this challenging.
are field guides and other scientific literature not also syntheses of their reference materials? they themselves have reference/citation lists. and we cite these syntheses all the time. the IDs you produce are your intellectual property as they are a product of your mind and efforts as an individual.
i do not regard myself as making a wholly original output: we stand on the shoulders of giants. my qualm is not at all that i feel entitled to credit. but citing workers is what we do in formal discussion and academia. synthesis is a process in which we consciously or unconsciously leave impressions of our own experiences/biases/oversights/etc. i would say we have a responsibility to others and to our sources to interpret/synthesize/represent the sources accurately. by listing what those sources are (and with it: who, what, when) anybody can independently verify the fidelity of your interpretation/synthesis. crediting the sources of our information (and those sources citing theirs) has been the basis of what we collectively know. and you are also a source!
to regard the question whether every ID needs to come with a reference list, of course not- many ID characteristics are generalized. we do not cite statements that the sky is blue as this is regarded as a readily observable fact. your name is attached to your ID and people can approach you directly for the reference list if they want to look further into those characters (perhaps they have a different understanding which conflicts, they are skeptical, or they simply want to learn more).
I donĀ“t see anything in your comment as to why this is more challenging with genAI (?)
You are just talking about best practice for comments and IDs - whether derived from human or AI.
A well-designed user-interface with an LLM could potentially do a better job than most humans of referencing every ID and comment it makes. The majority of people do not cite references for every single ID or comment they make ( e.g. in the 5-10 I looked at of the observations used by pisum above, there were no references given ). Humans donĀ“t always even remember the source of information for every ID they make or snippet of knowledge they retainā¦ or it might be an amalgam of sources. A genAI with inbuilt sources would be easier to cross-check. Sources should be a prerequisite for any implementation of genAI.
Also, fwiw the existing CV has no references at all for itĀ“s IDs, and nobody has ever challenged this before.
So, I donĀ“t see that genAI would make this specific problem more challenging than the existing system. It would more likely aid it than not. Hallucinated sources may be an issue, but againā¦ this can happen with humans and at least one can easily cross-check and flag these when they occur. I have told others and seen others tell me X because of Y, but then on further questioning realised I was/they were mistaken. Every ID / comment by AI can be supplemented with a word of caution about taking AI suggestions at face value. Human IDs and comments arenĀ“t currently supplemented with any words of caution even when they come from new users with zero experience.
Seeing an ID as IP is a stretch. ItĀ“s a statement of fact not an invention.
The comment is different as there is a unique creative element to how you write and interpret the facts.
Please - what is IP ?
I have seen comments - not my iD, blame it on CV LOL. Then pointed out, it is, your name on the ID.
The legal concept of intellectual property.