I think there are two different problems here:
- CV-followers: people who just follow the ID proposed by computer vision
- ID-happy: people who love to “agree” with identifications
When the two come together, the result is bad RG observations, which in turn feeds a vicious cycle of bad IDs: both by badly training the CV and by misleading people who are going beyond the CV proposals (by comparing with other potential species in the local species pool).
Regarding problem 1: CV-followers
If my experience is representative, most observations are entered using a mobile phone, and CV is quite irresistible to use (even when we know the species well, if we can’t quite remember the name).
Discussing with some iNat friends, some systematically pick the proposed ID even though they know full well that it may well be incorrect. This is particularly the case of people who don’t have much time: they just want to put the observation out there (quickly), and then leave it to the community to sort the ID. Which is fair enough!
In my case, because IDing is a big part of the fun with iNat, I use a more complex (time-consuming) method. When I enter observations of species I am not that familiar with, I first see what CV proposes, then go one or two taxonomic steps upwards and select that (e.g. pick the family of the proposed species, rather than the proposed species). This is almost always correct. Then, later on, I use the “compare” tool on the web interface to see what species in that taxon are found in my region/country, and try to refine the ID (or just leave it at a higher taxonomic level, if too daunting and/or if I don’t have much time).
In my opinion, CV should more often present suggestions at a higher-taxon level, whenever there is evidence that the ID is not simple (strong uncertainty). Examples:
- When the top suggestions are all over the place, with a wide diversity of taxa proposed and most (none…) of which are found in the region. For example (for an observation of Flexopecten flexuosus):
- When the proposed taxa have low levels of RG observations (e.g.: Xylocopa violacea) - which indicates they are often tough (or impossible) to identify.
- When the proposed species is commonly mistaken with another (e.g. see similar species to Anomia ephippium)
Regarding problem 2: ID-happy
Many observers are ID-happy: they assume identifiers are experts, and will readily “agree” (thus bringing the observation to RG). This has already been discussed in other threads (e.g. here). I am not sure what the solution is, but in my case the result is that I now refrain much more from IDing than I used to, focusing on the species I am most certain of, because I don’t want to be responsible for a wrong RG observation.
I recently found another type of ID-happy people: people who follow me, and then systematically agree with my IDs. I got suspicious as they never add any information (they never refine my IDs) yet that can agree with me even in tricky species (sometimes agreeing in minutes with something that took me 1h to ID). They are very active users with thousands of identifications (so not exactly newbies).
I contacted two of them to ask (very politely) if they were aware of iNat’s guidelines regarding identifications (An identification confirms that you can confidently identify it yourself compared to any possible lookalikes. Please do not simply “Agree” with an ID that someone else has made without confirming that you understand how to identify that taxon.). They were not. One thanked me for the information, said he would now focus on identifying species he knew well. The other was much less receptive. He told me iNat is also for non-experts (true) and that he is doing a service in supporting expert IDs by helping observations reach RG. He also told me that IDing allows him to follow an observation and thus learn if the ID is subsequently corrected (I recommended he faves the observation instead). The first one certainly seems to have changed his ID habits, I think the second too (less sure).
I am not sure what the solution is here, and I don’t want to discourage people who are often young and very well-meaning (and may be on their way to become specialists!). But I suspect they are attracted by the game aspect of iNat - the pleasure of seeing the ID numbers go up, their names in the top identifiers for a region/taxon… I know this has already been quite discussed, but I think one good approach would be to modify the way top identifiers are presented. If the idea is to point users to people who can help them with IDs, I would recommend that rather than presenting as the “top identifier” the person who did most IDs, to present the person who did most improving IDs (i.e., only those IDs that added new taxonomic information, and that were subsequently supported by a community consensus). These IDs are a much more accurate reflection of expertise. And I would include the IDs made by observers in this (as some observers are quite knowledgeable and give the good ID from the start).
If non-specialist users wanted to maximise their numbers of improving IDs (thus, gamify this), the easiest way is to ID at high taxonomic levels, either by refining observations currently at a very high level (e.g. from Insect to Lepidoptera) and/or by correcting wrong higher-taxa IDs (e.g. from bee to fly). This would be a really useful contribution to iNat, and (I suspect) a better way of nudging them towards learning how to correctly ID species.